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Abukuri DN. Novel Biomarkers for Alzheimer's Disease: Plasma Neurofilament Light and Cerebrospinal Fluid. Int J Alzheimers Dis 2024; 2024:6668159. [PMID: 38779175 PMCID: PMC11111307 DOI: 10.1155/2024/6668159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 03/18/2024] [Accepted: 04/24/2024] [Indexed: 05/25/2024] Open
Abstract
Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid-β peptide, tau proteins, CSF levels (Aβ, tau, and p-tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood-based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood-based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T-tau, P-tau, and Aβ42), CSF NFL, and plasma T-tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P-tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD-exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T-tau, P-tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended.
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Raj A, Torok J. Understanding the complex interplay between tau, amyloid and the network in the spatiotemporal progression of Alzheimer's Disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583407. [PMID: 38559176 PMCID: PMC10979926 DOI: 10.1101/2024.03.05.583407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
It is well known that Aβ and tau proteins are deposited stereotypically in brain regions to cause Alzheimer's disease. The interaction of amyloid and tau in neurodegenerative diseases is a central feature and key to understanding AD pathophysiology. However their mechanisms are controversial, and many aspects do not fit current theories that rely on cell-autonomous factors. While cell culture and animal studies point to various interaction mechanisms between amyloid and tau, their causal direction and mode (local, remote or network-mediated) remain unknown in human subjects. Further, cross-protein interaction is yet to be reconciled with canonical observations that the two species do not co-localize significantly either in space or in time, and do not target the same neuronal populations. To answer these questions quantitatively, in this study we employed a mathematical reaction-diffusion model encoding the biophysical mechanisms underlying self-assembly, trans-neuronal network propagation and enzymtic cross-species coupling of amyloid and tau. We first established that the spatiotemporal evolution of theoretical tau and Aβ correctly predicts empirical patterns of regional Aβ, tau and atrophy. Remarkably, the introduction of a 1-way Aβ→tau interaction was critical to the models' success. In comparison, both the non-interacting and the 2-way interaction models were significantly worse. We also found that network-mediated spread is essential; alternative modes of spread involving proximity or fiber length fare much worse. This mathematical exposition of the "pas de deux" of co-evolving proteins provides crucial quantitative and whole-brain support to the concept of amyloid-facilitated-tauopathy rather than the classic amyloid-cascade or pure-tau hypotheses, and helps explain certain known but poorly understood aspects of AD.
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Affiliation(s)
- Ashish Raj
- Department of Radiology, University of California at San Francisco, USA
- Bakar Computational Health Sciences Institute, UCSF
| | - Justin Torok
- Irving St, AC-116, Box 028, Parnassus Campus, San Francisco, CA 94122
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Maruyama H, Gomi M, Lwin TT, Yoneyama A, Sasaki T. [ 18F]-FDG uptake in brain slices prepared from an aged mouse model of Alzheimer's disease using a dynamic autoradiography technique. Ann Nucl Med 2024; 38:120-130. [PMID: 37921921 DOI: 10.1007/s12149-023-01879-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 09/29/2023] [Indexed: 11/05/2023]
Abstract
OBJECTIVE 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography ([18F]-FDG-PET) is a imaging modality that has been used to measure of glucose metabolism in the brain in Alzheimer's disease (AD). Clinically, decreased glucose uptake has been reported in the brain of AD, although the precise underlying mechanisms have not yet been elucidated. To elucidate the mechanisms of decreased [18F]-FDG uptake in the AD by PET, [18F]-FDG uptake in the brain of aged model mouse of AD was investigated using a dynamic autoradiography technique "bioradiography". A X-ray phase-contrast imaging (X-PCI) and a histopathological evaluation were also investigated to elucidate the mechanisms underlying the relationships between decreased [18F]-FDG uptake and the pathological changes in the brain of AD mouse. METHODS In this study, AD model mouse (5XFAD, APP+/PS1+) were used. [18F]-FDG-bioradiography was conducted in fresh slices of brain tissue under the condition of resting (slices immersed in 5 mM K+ solution) and metabolically active (in 50 mM K+ solution). Amyloid β42 (Aβ42) deposition in the brain of AD mouse was confirmed by X-PCI. In addition, the positive cells of phosphated tau protein (P-tau) and deposition of Aβ42 were also examined by immunohistochemical staining. RESULTS No significant differences were observed between the two groups in the resting condition. In the activate condition of the brain, [18F]-FDG uptake was significantly decreased in AD mice compared to WT mice. In X-PCI showed Aβ deposition in the AD mouse, but not in the WT. The AD mouse also showed increased P-tau, accumulation of Aβ42, increase in neuronal apoptosis, and decrease in the number of neurons than that of the WT mouse. CONCLUSION Neuronal damage, and induction of neuronal apoptosis, decreased [18F]-FDG uptake, increased Aβ accumulation and P-tau induced neurofibrillary degeneration are observed in AD mouse. In clinical diagnosis, reduction of [18F]-FDG uptake by PET is one of the means of diagnosing the onset of AD. Our results suggest that decreased uptake of [18F]-FDG in the brains of AD may be associated with neuronal dysfunction and cell death in the brain.
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Affiliation(s)
- Hiroko Maruyama
- Cytopathology, Graduate School of Medical Sciences, School of Allied Health Sciences, Kitasato University, 1-15-1 Kitasato, Minami-ku, Sagamihara, 252-0373, Japan.
| | - Misaki Gomi
- Cytopathology, Graduate School of Medical Sciences, Kitasato University, 1-15-1 Kitasato, Minami-ku, Sagamihara, 252-0373, Japan
| | - Thet-Thet Lwin
- Molecular Imaging, Graduate School of Medical Sciences, School of Allied Health Sciences, Kitasato University, 1-15-1 Kitasato, Minami-ku, Sagamihara, 252-0373, Japan
| | - Akio Yoneyama
- SAGA Light Source, Synchrotron Light Research Center, 8-7 Yayoigaoka, Tosu, Saga, 841-0005, Japan
| | - Toru Sasaki
- Radiation Safety Management, Graduate School of Medical Sciences, Kitasato University, 1-15-1 Kitasato, Minami-ku, Sagamihara, 252-0373, Japan
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Lin X, Feng T, Cui E, Li Y, Qin Z, Zhao X. A rat model established by simulating genetic-environmental interactions recapitulates human Alzheimer's disease pathology. Brain Res 2024; 1822:148663. [PMID: 37918702 DOI: 10.1016/j.brainres.2023.148663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/16/2023] [Accepted: 10/30/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND In humans, Alzheimer's disease (AD) is typically sporadic in nature, and its pathology is usually influenced by extensive factors. The study established a rat model based on the genetic-environmental interaction. METHODS A rat model was established by transduction of an adeno-associated virus combined with acrolein treatment. Rats were assigned to the normal control (NC), acrolein group, AAV (-) group, AAV-APP group, and AAV-APP/acrolein group. The success of model construction was verified in multiple ways, including by assessing cognitive function, examining microstructural changes in the brain in vivo, and performing immunohistochemistry. The contribution of genetic (APP mutation) and environmental (acrolein) factors to AD-like phenotypes in the model was explored by factorial analysis. RESULTS 1) The AAV-APP/acrolein group showed a decline in cognitive function, as indicated by a reduced gray matter volume in key cognition-related brain areas, lower FA values in the hippocampus and internal olfactory cortex, and Aβ deposition in the cortex and hippocampus. 2) The AAV-APP group also showed a decline in cognitive function, although the group exhibited atypical brain atrophy in the gray matter and insignificant Aβ deposition. 3) The acrolein group did not show any significant changes in Aβ levels, gray matter volume, or cognitive function. 4) The genetic factor (APP mutation) explained 39.74% of the AD-like phenotypes in the model factors, and the environmental factor (acrolein exposure) explained 33.3%. CONCLUSIONS The genetic-environmental interaction rat model exhibited a phenotype that resembled the features of human AD and will be useful for research on AD.
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Affiliation(s)
- Xiaomei Lin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Tianyuyi Feng
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Erheng Cui
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Yunfei Li
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Zhang Qin
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China
| | - Xiaohu Zhao
- Department of Imaging, The Fifth People's Hospital of Shanghai, Fudan University, Shanghai 200000, China.
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Crystal O, Maralani PJ, Black S, Fischer C, Moody AR, Khademi A. Detecting conversion from mild cognitive impairment to Alzheimer's disease using FLAIR MRI biomarkers. Neuroimage Clin 2023; 40:103533. [PMID: 37952286 PMCID: PMC10666029 DOI: 10.1016/j.nicl.2023.103533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 10/05/2023] [Accepted: 10/26/2023] [Indexed: 11/14/2023]
Abstract
Mild cognitive impairment (MCI) is the prodromal phase of Alzheimer's disease (AD) and while it presents as an imperative intervention window, it is difficult to detect which subjects convert to AD (cMCI) and which ones remain stable (sMCI). The objective of this work was to investigate fluid-attenuated inversion recovery (FLAIR) MRI biomarkers and their ability to differentiate between sMCI and cMCI subjects in cross-sectional and longitudinal data. Three types of biomarkers were investigated: volume, intensity and texture. Volume biomarkers included total brain volume, cerebrospinal fluid volume (CSF), lateral ventricular volume, white matter lesion volume, subarachnoid CSF, and grey matter (GM) and white matter (WM), all normalized to intracranial volume. The mean intensity, kurtosis, and skewness of the GM and WM made up the intensity features. Texture features quantified homogeneity and microstructural tissue changes of GM and WM regions. Composite indices were also considered, which are biomarkers that represent an aggregate sum (z-score normalization and summation) of all biomarkers. The FLAIR MRI biomarkers successfully identified high-risk subjects as significant differences (p < 0.05) were found between the means of the sMCI and cMCI groups and the rate of change over time for several individual biomarkers as well as the composite indices for both cross-sectional and longitudinal analyses. Classification accuracy and feature importance analysis showed volume biomarkers to be most predictive, however, best performance was obtained when complimenting the volume biomarkers with the intensity and texture features. Using all the biomarkers, accuracy of 86.2 % and 69.2 % was achieved for normal control-AD and sMCI-cMCI classification respectively. Survival analysis demonstrated that the majority of the biomarkers showed a noticeable impact on the AD conversion probability 4 years prior to conversion. Composite indices were the top performers for all analyses including feature importance, classification, and survival analysis. This demonstrated their ability to summarize various dimensions of disease into single-valued metrics. Significant correlation (p < 0.05) with phosphorylated-tau and amyloid-beta CSF biomarkers was found with all the FLAIR biomarkers. The proposed biomarker system is easily attained as FLAIR is routinely acquired, models are not computationally intensive and the results are explainable, thus making this pipeline easily integrated into clinical workflow.
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Affiliation(s)
- Owen Crystal
- Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada.
| | - Pejman J Maralani
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Sandra Black
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Hurvitz Brain Sciences Research Program, Sunnybrook Research Institute, Toronto, ON, Canada; Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; L.C. Campbell Cognitive Neurology Research Unit, Sunnybrook Health Sciences Centre, Toronto, ON, Canada; Department of Neurology, University of Toronto, Toronto, ON, Canada
| | - Corinne Fischer
- Institute of Medical Science, University of Toronto, Toronto, ON, Canada; Department of Psychiatry, St. Michael's Hospital, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada
| | - Alan R Moody
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - April Khademi
- Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada; Department of Medical Imaging, University of Toronto, Toronto, ON, Canada; Keenan Research Center, St. Michael's Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, Science and Technology (iBEST), Toronto, ON, Canada October 5, 2023; Vector Institute for Artificial Intelligence, Toronto, ON, Canada
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Wang YTT, Rosa-Neto P, Gauthier S. Advanced brain imaging for the diagnosis of Alzheimer disease. Curr Opin Neurol 2023; 36:481-490. [PMID: 37639461 DOI: 10.1097/wco.0000000000001198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
PURPOSE OF REVIEW The purpose is to review the latest advances of brain imaging for the diagnosis of Alzheimer disease (AD). RECENT FINDINGS Brain imaging techniques provide valuable and complementary information to support the diagnosis of Alzheimer disease in clinical and research settings. The recent FDA accelerated approvals of aducanumab, lecanemab and donanemab made amyloid-PET critical in helping determine the optimal window for anti-amyloid therapeutic interventions. Tau-PET, on the other hand, is considered of key importance for the tracking of disease progression and for monitoring therapeutic interventions in clinical trials. PET imaging for microglial activation, astrocyte reactivity and synaptic degeneration are still new techniques only used in the research field, and more studies are needed to validate their use in the clinical diagnosis of AD. Finally, artificial intelligence has opened new prospective in the early detection of AD using MRI modalities. SUMMARY Brain imaging techniques using PET improve our understanding of the different AD-related pathologies and their relationship with each other along the course of disease. With more robust validation, machine learning and deep learning algorithms could be integrated with neuroimaging modalities to serve as valuable tools for clinicians to make early diagnosis and prognosis of AD.
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Lepinay E, Cicchetti F. Tau: a biomarker of Huntington's disease. Mol Psychiatry 2023; 28:4070-4083. [PMID: 37749233 DOI: 10.1038/s41380-023-02230-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 07/31/2023] [Accepted: 08/11/2023] [Indexed: 09/27/2023]
Abstract
Developing effective treatments for patients with Huntington's disease (HD)-a neurodegenerative disorder characterized by severe cognitive, motor and psychiatric impairments-is proving extremely challenging. While the monogenic nature of this condition enables to identify individuals at risk, robust biomarkers would still be extremely valuable to help diagnose disease onset and progression, and especially to confirm treatment efficacy. If measurements of cerebrospinal fluid neurofilament levels, for example, have demonstrated use in recent clinical trials, other proteins may prove equal, if not greater, relevance as biomarkers. In fact, proteins such as tau could specifically be used to detect/predict cognitive affectations. We have herein reviewed the literature pertaining to the association between tau levels and cognitive states, zooming in on Alzheimer's disease, Parkinson's disease and traumatic brain injury in which imaging, cerebrospinal fluid, and blood samples have been interrogated or used to unveil a strong association between tau and cognition. Collectively, these areas of research have accrued compelling evidence to suggest tau-related measurements as both diagnostic and prognostic tools for clinical practice. The abundance of information retrieved in this niche of study has laid the groundwork for further understanding whether tau-related biomarkers may be applied to HD and guide future investigations to better understand and treat this disease.
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Affiliation(s)
- Eva Lepinay
- Centre de Recherche du CHU de Québec, Axe Neurosciences, Québec, QC, Canada
- Département de Psychiatrie & Neurosciences, Université Laval, Québec, QC, Canada
| | - Francesca Cicchetti
- Centre de Recherche du CHU de Québec, Axe Neurosciences, Québec, QC, Canada.
- Département de Psychiatrie & Neurosciences, Université Laval, Québec, QC, Canada.
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Kress GT, Popa ES, Thompson PM, Bookheimer SY, Thomopoulos SI, Ching CRK, Zheng H, Hirsh DA, Merrill DA, Panos SE, Raji CA, Siddarth P, Bramen JE. Preliminary validation of a structural magnetic resonance imaging metric for tracking dementia-related neurodegeneration and future decline. Neuroimage Clin 2023; 39:103458. [PMID: 37421927 PMCID: PMC10338152 DOI: 10.1016/j.nicl.2023.103458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline and atrophy in the medial temporal lobe (MTL) and subsequent brain regions. Structural magnetic resonance imaging (sMRI) has been widely used in research and clinical care for diagnosis and monitoring AD progression. However, atrophy patterns are complex and vary by patient. To address this issue, researchers have made efforts to develop more concise metrics that can summarize AD-specific atrophy. Many of these methods can be difficult to interpret clinically, hampering adoption. In this study, we introduce a novel index which we call an "AD-NeuroScore," that uses a modified Euclidean-inspired distance function to calculate differences between regional brain volumes associated with cognitive decline. The index is adjusted for intracranial volume (ICV), age, sex, and scanner model. We validated AD-NeuroScore using 929 older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, with a mean age of 72.7 years (SD = 6.3; 55.1-91.5) and cognitively normal (CN), mild cognitive impairment (MCI), or AD diagnoses. Our validation results showed that AD-NeuroScore was significantly associated with diagnosis and disease severity scores (measured by MMSE, CDR-SB, and ADAS-11) at baseline. Furthermore, baseline AD-NeuroScore was associated with both changes in diagnosis and disease severity scores at all time points with available data. The performance of AD-NeuroScore was equivalent or superior to adjusted hippocampal volume (AHV), a widely used metric in AD research. Further, AD-NeuroScore typically performed as well as or sometimes better when compared to other existing sMRI-based metrics. In conclusion, we have introduced a new metric, AD-NeuroScore, which shows promising results in detecting AD, benchmarking disease severity, and predicting disease progression. AD-NeuroScore differentiates itself from other metrics by being clinically practical and interpretable.
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Affiliation(s)
- Gavin T Kress
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Emily S Popa
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Susan Y Bookheimer
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Hong Zheng
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA 90292, USA
| | - Daniel A Hirsh
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
| | - David A Merrill
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; Department of Translational Neurosciences and Neurotherapeutics, Providence Saint John's Cancer Institute, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Stella E Panos
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA
| | - Cyrus A Raji
- Mallinckrodt Institute of Radiology, Washington University, St. Louis, MO, USA
| | - Prabha Siddarth
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA; UCLA Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California, Los Angeles, Westwood, CA 90095, USA
| | - Jennifer E Bramen
- Pacific Brain Health Center, Pacific Neuroscience Institute Foundation, Santa Monica, CA 90404, USA.
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Smith R, Cullen NC, Binette AP, Leuzy A, Blennow K, Zetterberg H, Klein G, Borroni E, Ossenkoppele R, Janelidze S, Palmqvist S, Mattsson-Carlgren N, Stomrud E, Hansson O. Tau-PET is superior to phospho-tau when predicting cognitive decline in symptomatic AD patients. Alzheimers Dement 2023; 19:2497-2507. [PMID: 36516028 PMCID: PMC10264552 DOI: 10.1002/alz.12875] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/15/2022] [Accepted: 10/21/2022] [Indexed: 12/15/2022]
Abstract
INTRODUCTION Biomarkers for the prediction of cognitive decline in patients with amnestic mild cognitive impairment (MCI) and amnestic mild dementia are needed for both clinical practice and clinical trials. METHODS We evaluated the ability of tau-PET (positron emission tomography), cortical atrophy on magnetic resonance imaging (MRI), baseline cognition, apolipoprotein E gene (APOE) status, plasma and cerebrospinal fluid (CSF) levels of phosphorylated tau-217, neurofilament light (NfL), and amyloid beta (Aβ)42/40 ratio (individually and in combination) to predict cognitive decline over 2 years in BioFINDER-2 and Alzheimer's Disease Neuroimaging Initiative (ADNI). RESULTS Baseline tau-PET and a composite baseline cognitive score were the strongest independent predictors of cognitive decline. Cortical thickness and NfL provided some additional information. Using a predictive algorithm to enrich patient selection in a theoretical clinical trial led to a significantly lower required sample size. DISCUSSION Models including baseline tau-PET and cognition consistently provided the best prediction of change in cognitive function over 2 years in patients with amnestic MCI or mild dementia.
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Affiliation(s)
- Ruben Smith
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
| | - Nicholas C. Cullen
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Alexa Pichet Binette
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Antoine Leuzy
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- UK Dementia Research Institute at UCL, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | | | | | | | - Rik Ossenkoppele
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Shorena Janelidze
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
| | - Sebastian Palmqvist
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Niklas Mattsson-Carlgren
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Erik Stomrud
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - Oskar Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences, Malmö, Lund University, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
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Rafii MS, Aisen PS. Detection and treatment of Alzheimer's disease in its preclinical stage. NATURE AGING 2023; 3:520-531. [PMID: 37202518 PMCID: PMC11110912 DOI: 10.1038/s43587-023-00410-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/29/2023] [Indexed: 05/20/2023]
Abstract
Longitudinal multimodal biomarker studies reveal that the continuum of Alzheimer's disease (AD) includes a long latent phase, referred to as preclinical AD, which precedes the onset of symptoms by decades. Treatment during the preclinical AD phase offers an optimal opportunity for slowing the progression of disease. However, trial design in this population is complex. In this Review, we discuss the recent advances in accurate plasma measurements, new recruitment approaches, sensitive cognitive instruments and self-reported outcomes that have facilitated the successful launch of multiple phase 3 trials for preclinical AD. The recent success of anti-amyloid immunotherapy trials in symptomatic AD has increased the enthusiasm for testing this strategy at the earliest feasible stage. We provide an outlook for standard screening of amyloid accumulation at the preclinical stage in clinically normal individuals, during which effective therapy to delay or prevent cognitive decline can be initiated.
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Affiliation(s)
- Michael S Rafii
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, Los Angeles, CA, USA.
| | - Paul S Aisen
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine University of Southern California, Los Angeles, CA, USA
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11
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Hu X, Meier M, Pruessner J. Challenges and opportunities of diagnostic markers of Alzheimer's disease based on structural magnetic resonance imaging. Brain Behav 2023; 13:e2925. [PMID: 36795041 PMCID: PMC10013953 DOI: 10.1002/brb3.2925] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 02/04/2023] [Indexed: 02/17/2023] Open
Abstract
OBJECTIVES This article aimed to carry out a narrative literature review of early diagnostic markers of Alzheimer's disease (AD) based on both micro and macro levels of pathology, indicating the shortcomings of current biomarkers and proposing a novel biomarker of structural integrity that associates the hippocampus and adjacent ventricle together. This could help to reduce the influence of individual variety and improve the accuracy and validity of structural biomarker. METHODS This review was based on presenting comprehensive background of early diagnostic markers of AD. We have compiled those markers into micro level and macro level, and discussed the advantages and disadvantages of them. Eventually the ratio of gray matter volume to ventricle volume was put forward. RESULTS The costly methodologies and related high patient burden of "micro" biomarkers (cerebrospinal fluid biomarkers) hinder the implementation in routine clinical examination. In terms of "macro" biomarkers- hippocampal volume (HV), there is a large variation of it among population, which undermines its validity Considering the gray matter atrophies while the adjacent ventricular volume enlarges, we assume the hippocampal to ventricle ratio (HVR) is a more reliable marker than HV alone the emerging evidence showed hippocampal to ventricle ratio predicts memory functions better than HV alone in elderly sample. CONCLUSIONS The ratio between gray matter structures and adjacent ventricular volumes counts as a promising superior diagnostic marker of early neurodegeneration.
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Affiliation(s)
- Xiang Hu
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Maria Meier
- Department of Psychology, University of Konstanz, Konstanz, Germany
| | - Jens Pruessner
- Department of Psychology, University of Konstanz, Konstanz, Germany
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12
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Mavragani A, Bruhin LC, Schütz N, Naef AC, Hegi H, Reuse P, Schindler KA, Krack P, Wiest R, Chan A, Nef T, Gerber SM. Development of an Open-source and Lightweight Sensor Recording Software System for Conducting Biomedical Research: Technical Report. JMIR Form Res 2023; 7:e43092. [PMID: 36800219 PMCID: PMC9985000 DOI: 10.2196/43092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/28/2022] [Accepted: 01/03/2023] [Indexed: 02/18/2023] Open
Abstract
BACKGROUND Digital sensing devices have become an increasingly important component of modern biomedical research, as they help provide objective insights into individuals' everyday behavior in terms of changes in motor and nonmotor symptoms. However, there are significant barriers to the adoption of sensor-enhanced biomedical solutions in terms of both technical expertise and associated costs. The currently available solutions neither allow easy integration of custom sensing devices nor offer a practicable methodology in cases of limited resources. This has become particularly relevant, given the need for real-time sensor data that could help lower health care costs by reducing the frequency of clinical assessments performed by specialists and improve access to health assessments (eg, for people living in remote areas or older adults living at home). OBJECTIVE The objective of this paper is to detail the end-to-end development of a novel sensor recording software system that supports the integration of heterogeneous sensor technologies, runs as an on-demand service on consumer-grade hardware to build sensor systems, and can be easily used to reliably record longitudinal sensor measurements in research settings. METHODS The proposed software system is based on a server-client architecture, consisting of multiple self-contained microservices that communicated with each other (eg, the web server transfers data to a database instance) and were implemented as Docker containers. The design of the software is based on state-of-the-art open-source technologies (eg, Node.js or MongoDB), which fulfill nonfunctional requirements and reduce associated costs. A series of programs to facilitate the use of the software were documented. To demonstrate performance, the software was tested in 3 studies (2 gait studies and 1 behavioral study assessing activities of daily living) that ran between 2 and 225 days, with a total of 114 participants. We used descriptive statistics to evaluate longitudinal measurements for reliability, error rates, throughput rates, latency, and usability (with the System Usability Scale [SUS] and the Post-Study System Usability Questionnaire [PSSUQ]). RESULTS Three qualitative features (event annotation program, sample delay analysis program, and monitoring dashboard) were elaborated and realized as integrated programs. Our quantitative findings demonstrate that the system operates reliably on consumer-grade hardware, even across multiple months (>420 days), providing high throughput (2000 requests per second) with a low latency and error rate (<0.002%). In addition, the results of the usability tests indicate that the system is effective, efficient, and satisfactory to use (mean usability ratings for the SUS and PSSUQ were 89.5 and 1.62, respectively). CONCLUSIONS Overall, this sensor recording software could be leveraged to test sensor devices, as well as to develop and validate algorithms that are able to extract digital measures (eg, gait parameters or actigraphy). The proposed software could help significantly reduce barriers related to sensor-enhanced biomedical research and allow researchers to focus on the research questions at hand rather than on developing recording technologies.
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Affiliation(s)
| | - Lena C Bruhin
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Narayan Schütz
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,DomoHealth SA, Lausanne, Switzerland
| | - Aileen C Naef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Heinz Hegi
- Department of Sport Science, University of Bern, Bern, Switzerland
| | - Pascal Reuse
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
| | - Kaspar A Schindler
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Paul Krack
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Andrew Chan
- Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Tobias Nef
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.,Department of Neurology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Stephan M Gerber
- Gerontechnology and Rehabilitation Group, ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland
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13
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Stark J, Hiersche KJ, Yu JC, Hasselbach AN, Abdi H, Hayes SM. Partial Least Squares Regression Analysis of Alzheimer's Disease Biomarkers, Modifiable Health Variables, and Cognitive Change in Older Adults with Mild Cognitive Impairment. J Alzheimers Dis 2023; 93:633-651. [PMID: 37066909 PMCID: PMC10999056 DOI: 10.3233/jad-221084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
BACKGROUND Prior work has shown that certain modifiable health, Alzheimer's disease (AD) biomarker, and demographic variables are associated with cognitive performance. However, less is known about the relative importance of these different domains of variables in predicting longitudinal change in cognition. OBJECTIVE Identify novel relationships between modifiable physical and health variables, AD biomarkers, and slope of cognitive change over two years in a cohort of older adults with mild cognitive impairment (MCI). METHODS Metrics of cardiometabolic risk, stress, inflammation, neurotrophic/growth factors, and AD pathology were assessed in 123 older adults with MCI at baseline from the Alzheimer's Disease Neuroimaging Initiative (mean age = 73.9; SD = 7.6; mean education = 16.0; SD = 3.0). Partial least squares regression (PLSR)-a multivariate method which creates components that best predict an outcome-was used to identify whether these physiological variables were important in predicting slope of change in episodic memory or executive function over two years. RESULTS At two-year follow-up, the two PLSR models predicted, respectively, 20.0% and 19.6% of the variance in change in episodic memory and executive function. Baseline levels of AD biomarkers were important in predicting change in both episodic memory and executive function. Baseline education and neurotrophic/growth factors were important in predicting change in episodic memory, whereas cardiometabolic variables such as blood pressure and cholesterol were important in predicting change in executive function. CONCLUSION These data-driven analyses highlight the impact of AD biomarkers on cognitive change and further clarify potential domain specific relationships with predictors of cognitive change.
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Affiliation(s)
- Jessica Stark
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Kelly J Hiersche
- Department of Psychology, The Ohio State University, Columbus, OH, USA
| | - Ju-Chi Yu
- Centre for Addiction and Mental Health, Toronto, Canada
| | | | - Hervé Abdi
- Department of Psychology, The University of Texas at Dallas, Dallas, TX, USA
| | - Scott M Hayes
- Department of Psychology, The Ohio State University, Columbus, OH, USA
- Department of Psychology, The Ohio State University, Columbus, OH, USA
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14
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Wang H, Li Q, Liu Y. Regularized Buckley-James method for right-censored outcomes with block-missing multimodal covariates. Stat (Int Stat Inst) 2022; 11:e515. [PMID: 37854542 PMCID: PMC10583730 DOI: 10.1002/sta4.515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/10/2022] [Indexed: 10/20/2023]
Abstract
High-dimensional data with censored outcomes of interest are prevalent in medical research. To analyze such data, the regularized Buckley-James estimator has been successfully applied to build accurate predictive models and conduct variable selection. In this paper, we consider the problem of parameter estimation and variable selection for the semiparametric accelerated failure time model for high-dimensional block-missing multimodal neuroimaging data with censored outcomes. We propose a penalized Buckley-James method that can simultaneously handle block-wise missing covariates and censored outcomes. This method can also perform variable selection. The proposed method is evaluated by simulations and applied to a multimodal neuroimaging dataset and obtains meaningful results.
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Affiliation(s)
- Haodong Wang
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA
| | - Quefeng Li
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, 27516, North Carolina, USA
| | - Yufeng Liu
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, 27599, North Carolina, USA
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, 27516, North Carolina, USA
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, 27599-7264, North Carolina, USA
- Carolina Center for Genome Sciences, The University of North Carolina at Chapel Hill, Chapel Hill, 27514, North Carolina, USA
- Lineberger Comprehensive Cancer Center, The University of North Carolina at Chapel Hill, Chapel Hill, 27514, North Carolina, USA
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15
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Lee K, Choi KM, Park S, Lee SH, Im CH. Selection of the optimal channel configuration for implementing wearable EEG devices for the diagnosis of mild cognitive impairment. Alzheimers Res Ther 2022; 14:170. [DOI: 10.1186/s13195-022-01115-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Accepted: 10/31/2022] [Indexed: 11/15/2022]
Abstract
Abstract
Background
Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI.
Method
We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition.
Results
The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets.
Conclusions
This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability.
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16
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Zhang Y, Liu T, Lanfranchi V, Yang P. Explainable Tensor Multi-Task Ensemble Learning Based on Brain Structure Variation for Alzheimer's Disease Dynamic Prediction. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 11:1-12. [PMID: 36478772 PMCID: PMC9721355 DOI: 10.1109/jtehm.2022.3219775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/11/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
Abstract
Machine learning approaches for predicting Alzheimer's disease (AD) progression can substantially assist researchers and clinicians in developing effective AD preventive and treatment strategies. This study proposes a novel machine learning algorithm to predict the AD progression utilising a multi-task ensemble learning approach. Specifically, we present a novel tensor multi-task learning (MTL) algorithm based on similarity measurement of spatio-temporal variability of brain biomarkers to model AD progression. In this model, the prediction of each patient sample in the tensor is set as one task, where all tasks share a set of latent factors obtained through tensor decomposition. Furthermore, as subjects have continuous records of brain biomarker testing, the model is extended to ensemble the subjects' temporally continuous prediction results utilising a gradient boosting kernel to find more accurate predictions. We have conducted extensive experiments utilising data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to evaluate the performance of the proposed algorithm and model. Results demonstrate that the proposed model have superior accuracy and stability in predicting AD progression compared to benchmarks and state-of-the-art multi-task regression methods in terms of the Mini Mental State Examination (MMSE) questionnaire and The Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) cognitive scores. Brain biomarker correlation information can be utilised to identify variations in individual brain structures and the model can be utilised to effectively predict the progression of AD with magnetic resonance imaging (MRI) data and cognitive scores of AD patients at different stages.
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Affiliation(s)
- Yu Zhang
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
| | - Tong Liu
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
| | | | - Po Yang
- Department of Computer ScienceThe University of Sheffield Sheffield S10 2TN U.K
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17
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Xu L, Wu H, He C, Wang J, Zhang C, Nie F, Chen L. Multi-modal sequence learning for Alzheimer’s disease progression prediction with incomplete variable-length longitudinal data. Med Image Anal 2022; 82:102643. [DOI: 10.1016/j.media.2022.102643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Revised: 08/27/2022] [Accepted: 09/23/2022] [Indexed: 11/28/2022]
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18
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He Q, Shi L, Luo Y, Wan C, Malone IB, Mok VCT, Cole JH, Anatürk M. Validation of the Alzheimer's disease-resemblance atrophy index in classifying and predicting progression in Alzheimer's disease. Front Aging Neurosci 2022; 14:932125. [PMID: 36062150 PMCID: PMC9435378 DOI: 10.3389/fnagi.2022.932125] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 07/14/2022] [Indexed: 12/01/2022] Open
Abstract
Background Automated tools for characterising dementia risk have the potential to aid in the diagnosis, prognosis, and treatment of Alzheimer's disease (AD). Here, we examined a novel machine learning-based brain atrophy marker, the AD-resemblance atrophy index (AD-RAI), to assess its test-retest reliability and further validate its use in disease classification and prediction. Methods Age- and sex-matched 44 probable AD (Age: 69.13 ± 7.13; MMSE: 27-30) and 22 non-demented control (Age: 69.38 ± 7.21; MMSE: 27-30) participants were obtained from the Minimal Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) dataset. Serial T1-weighted images (n = 678) from up to nine time points over a 2-year period, including 179 pairs of back-to-back scans acquired on same participants on the same day and 40 pairs of scans acquired at 2-week intervals were included. All images were automatically processed with AccuBrain® to calculate the AD-RAI. Its same-day repeatability and 2-week reproducibility were first assessed. The discriminative performance of AD-RAI was evaluated using the receiver operating characteristic curve, where DeLong's test was used to evaluate its performance against quantitative medial temporal lobe atrophy (QMTA) and hippocampal volume adjusted by intracranial volume (ICV)-proportions and ICV-residuals methods, respectively (HVR and HRV). Linear mixed-effects modelling was used to investigate longitudinal trajectories of AD-RAI and baseline AD-RAI prediction of cognitive decline. Finally, the longitudinal associations between AD-RAI and MMSE scores were assessed. Results AD-RAI had excellent same-day repeatability and excellent 2-week reproducibility. AD-RAI's AUC (99.8%; 95%CI = [99.3%, 100%]) was equivalent to that of QMTA (96.8%; 95%CI = [92.9%, 100%]), and better than that of HVR (86.8%; 95%CI = [78.2%, 95.4%]) or HRV (90.3%; 95%CI = [83.0%, 97.6%]). While baseline AD-RAI was significantly higher in the AD group, it did not show detectable changes over 2 years. Baseline AD-RAI was negatively associated with MMSE scores and the rate of the change in MMSE scores over time. A negative longitudinal association was also found between AD-RAI values and the MMSE scores among AD patients. Conclusions The AD-RAI represents a potential biomarker that may support AD diagnosis and be used to predict the rate of future cognitive decline in AD patients.
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Affiliation(s)
- Qiling He
- UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Lin Shi
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Yishan Luo
- BrainNow Research Institute, Shenzhen, China
| | - Chao Wan
- School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Ian B. Malone
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Vincent C. T. Mok
- Gerald Choa Neuroscience Centre, Therese Pei Fong Chow Research Centre for Prevention of Dementia, Lui Che Woo Institute of Innovative Medicine, Division of Neurology, Department of Medicine and Therapeutics, Prince of Wales Hospital, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - James H. Cole
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Dementia Research Centre, Institute of Neurology, University College London, London, United Kingdom
| | - Melis Anatürk
- Department of Computer Science, Faculty of Engineering Science, University College London, London, United Kingdom
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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Incremental diagnostic value of 18F-Fluetemetamol PET in differential diagnoses of Alzheimer's Disease-related neurodegenerative diseases from an unselected memory clinic cohort. Sci Rep 2022; 12:10385. [PMID: 35725910 PMCID: PMC9209498 DOI: 10.1038/s41598-022-14532-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 06/08/2022] [Indexed: 11/08/2022] Open
Abstract
To evaluate the incremental diagnostic value of 18F-Flutemetamol PET following MRI measurements on an unselected prospective cohort collected from a memory clinic. A total of 84 participants was included in this study. A stepwise study design was performed including initial analysis (based on clinical assessments), interim analysis (revision of initial analysis post-MRI) and final analysis (revision of interim analysis post-18F-Flutemetamol PET). At each time of evaluation, every participant was categorized into SCD, MCI or dementia syndromal group and further into AD-related, non-AD related or non-specific type etiological subgroup. Post 18F-Flutemetamol PET, the significant changes were seen in the syndromal MCI group (57%, p < 0.001) involving the following etiological subgroups: AD-related MCI (57%, p < 0.01) and non-specific MCI (100%, p < 0.0001); and syndromal dementia group (61%, p < 0.0001) consisting of non-specific dementia subgroup (100%, p < 0.0001). In the binary regression model, amyloid status significantly influenced the diagnostic results of interim analysis (p < 0.01). 18F-Flutemetamol PET can have incremental value following MRI measurements, particularly reflected in the change of diagnosis of individuals with unclear etiology and AD-related-suspected patients due to the role in complementing AD-related pathological information.
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20
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Deep Learning-Based Diagnosis of Alzheimer’s Disease. J Pers Med 2022; 12:jpm12050815. [PMID: 35629237 PMCID: PMC9143671 DOI: 10.3390/jpm12050815] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2022] [Revised: 05/15/2022] [Accepted: 05/16/2022] [Indexed: 12/27/2022] Open
Abstract
Alzheimer’s disease (AD), the most familiar type of dementia, is a severe concern in modern healthcare. Around 5.5 million people aged 65 and above have AD, and it is the sixth leading cause of mortality in the US. AD is an irreversible, degenerative brain disorder characterized by a loss of cognitive function and has no proven cure. Deep learning techniques have gained popularity in recent years, particularly in the domains of natural language processing and computer vision. Since 2014, these techniques have begun to achieve substantial consideration in AD diagnosis research, and the number of papers published in this arena is rising drastically. Deep learning techniques have been reported to be more accurate for AD diagnosis in comparison to conventional machine learning models. Motivated to explore the potential of deep learning in AD diagnosis, this study reviews the current state-of-the-art in AD diagnosis using deep learning. We summarize the most recent trends and findings using a thorough literature review. The study also explores the different biomarkers and datasets for AD diagnosis. Even though deep learning has shown promise in AD diagnosis, there are still several challenges that need to be addressed.
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21
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Feng X, Provenzano FA, Small SA. A deep learning MRI approach outperforms other biomarkers of prodromal Alzheimer's disease. Alzheimers Res Ther 2022; 14:45. [PMID: 35351193 PMCID: PMC8966329 DOI: 10.1186/s13195-022-00985-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/07/2022] [Indexed: 11/10/2022]
Abstract
BACKGROUND The three core pathologies of Alzheimer's disease (AD) are amyloid pathology, tau pathology, and neurodegeneration. Biomarkers exist for each. Neurodegeneration is often detected by neuroimaging, and we hypothesized that a voxel-based deep learning approach using structural MRI might outperform other neuroimaging methods. METHODS First, we implement an MRI-based deep learning model, trained with a data augmentation strategy, which classifies Alzheimer's dementia and generates class activation maps. Next, we tested the model in prodromal AD and compared its performance to other biomarkers of amyloid pathology, tau pathology, and neuroimaging biomarkers of neurodegeneration. RESULTS The model distinguished between controls and AD with high accuracy (AUROC = 0.973) with class activation maps that localized to the hippocampal formation. As hypothesized, the model also outperformed other neuroimaging biomarkers of neurodegeneration in prodromal AD (AUROC = 0.788) but also outperformed biomarkers of amyloid (CSF Aβ = 0.702) or tau pathology (CSF tau = 0.682), and the findings are interpreted in the context of AD's known anatomical biology. CONCLUSIONS The advantages of using deep learning to extract biomarker information from conventional MRIs extend practically, potentially reducing patient burden, risk, and cost.
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Affiliation(s)
- Xinyang Feng
- Department of Biomedical Engineering, Columbia University, New York, NY, 10027, USA
- Current address: Meta Platforms, Inc., Menlo Park, CA, USA
| | - Frank A Provenzano
- Department of Neurology, Columbia University, 630 West 168th Street, P&S Box 16, New York, NY, 10032, USA
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, 10032, USA
| | - Scott A Small
- Department of Neurology, Columbia University, 630 West 168th Street, P&S Box 16, New York, NY, 10032, USA.
- Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, 10032, USA.
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22
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Lobo JD, Moore DJ, Bondi MW, Soontornniyomkij V, Soontornniyomkij B, Gouaux B, Achim CL, Ellis RJ, Sundermann EE. CSF markers of AD-related pathology relate specifically to memory impairment in older people with HIV: a pilot study. J Neurovirol 2022; 28:162-167. [PMID: 35103880 PMCID: PMC9081235 DOI: 10.1007/s13365-021-01048-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 12/07/2021] [Accepted: 12/18/2021] [Indexed: 02/03/2023]
Abstract
Given the co-occurrence of memory impairment in HIV-associated neurocognitive disorders (HAND) and amnestic mild cognitive impairment/Alzheimer's disease (aMCI/AD), biomarkers are needed that can disentangle these conditions among people with HIV (PWH). We assessed whether cerebrospinal fluid (CSF) markers of AD could help in this effort by determining their relationship to learning and memory deficits versus cognitive deficits more characteristic of HAND than aMCI/AD (processing speed and complex visual/motor coordination) among 31 older PWH. CSF amyloid-β42 phosphorylated-tau, amyloid-β40/amyloid-β42 and phosphorylated-tau/amyloid-β42 ratio related to learning/memory performance but not HAND-related deficits, suggesting that these biomarkers may have utility in disentangling aMCI/AD from HAND.
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Affiliation(s)
- Judith D Lobo
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA.
| | - David J Moore
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA
| | - Mark W Bondi
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA
- Psychology Service, VA San Diego Healthcare System, San Diego, USA
| | | | | | - Ben Gouaux
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA
| | - Cristian L Achim
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA
- Department of Pathology, University of California, San Diego, USA
| | - Ronald J Ellis
- Department of Neurosciences, University of California, San Diego, USA
| | - Erin E Sundermann
- Department of Psychiatry, University of California, 220 Dickinson St, #B, San Diego, CA, 92103, USA
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Dong L, Mao C, Liu C, Li J, Huang X, Wang J, Lei D, Chu S, Sha L, Xu Q, Peng B, Cui L, Gao J. Association Between Common Variants of APOE, ABCA7, A2M, BACE1, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: Data from the PUMCH Dementia Cohort. J Alzheimers Dis 2021; 85:1511-1518. [PMID: 34958020 PMCID: PMC8925115 DOI: 10.3233/jad-215067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: The previous studies have identified several genes in relation to Alzheimer’s disease (AD), such as ABCA7, CR1, etc. A few studies have explored the association between the common variants, mainly in the non-coding regions of these genes, and cerebrospinal fluid (CSF) biomarkers. Fewer studies target the variants in the coding regions. Objective: To illustrate the association between the common variants within or adjacent to the coding regions of AD susceptible genes and CSF biomarkers in AD patients. Methods: 75 sporadic probable AD patients were extracted from the dementia cohort of Peking Union Medical College Hospital. They all had history inquiry, physical examination, blood test, cognitive assessment, brain MRI, CSF testing of Aβ42, 181p-tau, and t-tau, and next-generation DNA sequencing. Sixty-nine common single nucleotide polymorphisms (SNPs) (minor allele frequency > 0.01) within or near the coding region of 13 AD susceptible genes were included in the analysis. Results: The rs7412-CC (APOE) genotype showed lower CSF Aβ42 level and higher p-tau/Aβ42 ratio than the rs7412-CT genotype. The rs3752246-C (ABCA7) allele correlated with lower CSF Aβ42 level. The alternate alleles of six ABCA7 SNPs were related to lower CSF p-tau, including rs3745842, rs3764648, rs3764652, rs4147930, rs4147934 and rs881768. The rs11609582-TT (A2M) genotype showed higher CSF p-tau than the rs11609582-TA genotype. The p-tau/Aβ42 ratio was higher in the rs490460-TT (BACE1) genotype relative to the rs490460-GT genotype. Conclusion: Some common variants within or near the coding regions of APOE, ABCA7, A2M, and BACE1 are associated with CSF Aβ42, p-tau. or p-tau/Aβ42.
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Affiliation(s)
- Liling Dong
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chenhui Mao
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Caiyan Liu
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Li
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Wang
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dan Lei
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Longze Sha
- Institute of Basic Medical Sciences, Peking Union Medical College, Beijing, China
| | - Qi Xu
- Institute of Basic Medical Sciences, Peking Union Medical College, Beijing, China
| | - Bin Peng
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liying Cui
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jing Gao
- Neurology Department, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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Ingala S, Tomassen J, Collij LE, Prent N, van 't Ent D, Ten Kate M, Konijnenberg E, Yaqub M, Scheltens P, de Geus EJC, Teunissen CE, Tijms B, Wink AM, Barkhof F, van Berckel BNM, Visser PJ, den Braber A. Amyloid-driven disruption of default mode network connectivity in cognitively healthy individuals. Brain Commun 2021; 3:fcab201. [PMID: 34617016 PMCID: PMC8490784 DOI: 10.1093/braincomms/fcab201] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 04/06/2021] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Cortical accumulation of amyloid beta is one of the first events of Alzheimer’s disease pathophysiology, and has been suggested to follow a consistent spatiotemporal ordering, starting in the posterior cingulate cortex, precuneus and medio-orbitofrontal cortex. These regions overlap with those of the default mode network, a brain network also involved in memory functions. Aberrant default mode network functional connectivity and higher network sparsity have been reported in prodromal and clinical Alzheimer’s disease. We investigated the association between amyloid burden and default mode network connectivity in the preclinical stage of Alzheimer’s disease and its association with longitudinal memory decline. We included 173 participants, in which amyloid burden was assessed both in CSF by the amyloid beta 42/40 ratio, capturing the soluble part of amyloid pathology, and in dynamic PET scans calculating the non-displaceable binding potential in early-stage regions. The default mode network was identified with resting-state functional MRI. Then, we calculated functional connectivity in the default mode network, derived from independent component analysis, and eigenvector centrality, a graph measure recursively defining important nodes on the base of their connection with other important nodes. Memory was tested at baseline, 2- and 4-year follow-up. We demonstrated that higher amyloid burden as measured by both CSF amyloid beta 42/40 ratio and non-displaceable binding potential in the posterior cingulate cortex was associated with lower functional connectivity in the default mode network. The association between amyloid burden (CSF and non-displaceable binding potential in the posterior cingulate cortex) and aberrant default mode network connectivity was confirmed at the voxel level with both functional connectivity and eigenvector centrality measures, and it was driven by voxel clusters localized in the precuneus, cingulate, angular and left middle temporal gyri. Moreover, we demonstrated that functional connectivity in the default mode network predicts longitudinal memory decline synergistically with regional amyloid burden, as measured by non-displaceable binding potential in the posterior cingulate cortex. Taken together, these results suggest that early amyloid beta deposition is associated with aberrant default mode network connectivity in cognitively healthy individuals and that default mode network connectivity markers can be used to identify subjects at risk of memory decline.
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Affiliation(s)
- Silvia Ingala
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Jori Tomassen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Lyduine E Collij
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Naomi Prent
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands.,Faculty of Behavioral and Movement Sciences, Section Clinical Neuropsychology, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.,Vesalius, Centre for Neuropsychiatry, GGZ Altrecht, 3447 GM Woerden, The Netherlands
| | - Dennis van 't Ent
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Mara Ten Kate
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Elles Konijnenberg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Maqsood Yaqub
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Eco J C de Geus
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Charlotte E Teunissen
- Department of Clinical Chemistry, Neurochemistry Laboratory, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC, 1081 HV Amsterdam, The Netherlands
| | - Betty Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Alle Meije Wink
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands.,Institute of Neurology and Healthcare Engineering, University College London, WC1E 6BT London, UK
| | - Bart N M van Berckel
- Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Pieter Jelle Visser
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands
| | - Anouk den Braber
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Location VUmc, 1081 HZ Amsterdam, The Netherlands.,Department of Biological Psychology, Vrije Universiteit Amsterdam, Neuroscience Amsterdam, 1081 HV Amsterdam, The Netherlands
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25
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Llano DA, Devanarayan V. Serum Phosphatidylethanolamine and Lysophosphatidylethanolamine Levels Differentiate Alzheimer's Disease from Controls and Predict Progression from Mild Cognitive Impairment. J Alzheimers Dis 2021; 80:311-319. [PMID: 33523012 DOI: 10.3233/jad-201420] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND There is intense interest in the development of blood-based biomarkers, not only that can differentiate Alzheimer's disease (AD) from controls, but that can also predict conversion from mild cognitive impairment (MCI) to AD. Serum biomarkers carry the potential advantage over imaging or spinal fluid markers both in terms of cost and invasiveness. OBJECTIVE Our objective was to measure the potential for serum lipid markers to differentiate AD from age-matched healthy controls as well as to predict conversion from MCI to AD. METHODS Using a publicly-available dataset, we examined the relationship between baseline serum levels of 349 known lipids from 16 classes of lipids to differentiate disease state as well as to predict the conversion from MCI to AD. RESULTS We observed that several classes of lipids (cholesteroyl ester, phosphatidylethanolamine, lysophosphatidylethanolamine, and acylcarnitine) differentiated AD from normal controls. Among these, only two classes, phosphatidylethanolamine (PE) and lysophosphatidylethanolamine (lyso-PE), predicted time to conversion from MCI to AD. Low levels of PE and high levels of lyso-PE result in two-fold faster median time to progression from MCI to AD, with hazard ratios 0.62 and 1.34, respectively. CONCLUSION These data suggest that serum PE and lyso-PE may be useful biomarkers for predicting MCI to AD conversion. In addition, since PE is converted to lyso-PE by phospholipase A2, an important inflammatory mediator that is dysregulated in AD, these data suggest that the disrupted serum lipid profile here may be related to an abnormal inflammatory response early in the AD pathologic cascade.
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Affiliation(s)
- Daniel A Llano
- Molecular and Cellular Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA.,Carle Neuroscience Institute, Urbana, IL, USA
| | - Viswanath Devanarayan
- GlaxoSmithKline, Collegeville, PA, USA.,Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, Chicago, IL, USA
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26
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Cheng B, Zhu B, Pu S. Multi-auxiliary domain transfer learning for diagnosis of MCI conversion. Neurol Sci 2021; 43:1721-1739. [PMID: 34510292 DOI: 10.1007/s10072-021-05568-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 08/14/2021] [Indexed: 01/18/2023]
Abstract
In the early stage of Alzheimer's disease (AD), mild cognitive impairment (MCI) has a higher risk of progression to AD, so the prediction of whether an MCI subject will progress to AD (known as progressive MCI, PMCI) or not (known as stable MCI, SMCI) within a certain period is particularly important in practice. It is known that such a task could benefit from jointly learning-related auxiliary tasks such as differentiating AD from PMCI or PMCI from normal control (NC) in order to take full advantage of their shared commonality. However, few existing methods along this line fully consider the correlations between the target and auxiliary tasks according to the clinical practice of AD pathology for diagnosis. To deal with this problem, in this paper, treating each task domain as a different one, we borrow the idea from transfer learning and propose a novel multi-auxiliary domain transfer learning (MaDTL) method, which explicitly utilizes the correlations between the target domain (task) and multi-auxiliary domains (tasks) according to the clinical practice. Specifically, the proposed MaDTL method incorporates two key modules. The first one is a multi-auxiliary domain transfer-based feature selection (MaDTFS) model, which can select a discriminative feature subset shared by the target domain and the multi-auxiliary domains. In the MaDTFS model, to combine more training data from multi-auxiliary domains and simultaneously suppress the negative effects resulting from the irrelevant parts of multi-auxiliary domains, we proposed a sparse group correlation Lasso that includes a proposed group correlation Lasso penalty (i.e., [Formula: see text]) and a proposed correlation Lasso penalty (i.e., [Formula: see text]). The second module in MaDTL is a multi-auxiliary domain transfer-based classification (MaDTC) model that improves the voting with linear weighting-based ensemble learning. This model extends the constraints of the linear weighting method so that it can simultaneously combine training data from multi-auxiliary domains and achieve a robust classifier by minimizing negative effects from the irrelevant part of multi-auxiliary domains. Experimental results on 409 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with the baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data validate the effectiveness of the proposed method by significantly improving the classification accuracy to 80.37% for the identification of MCI-to-AD conversion, outperforming the state-of-the-art methods.
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Affiliation(s)
- Bo Cheng
- Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China.
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China.
| | - Bingli Zhu
- College of Computer Science and Engineering, Chongqing Three Gorges University, Chongqing, 404100, China
| | - Shuchang Pu
- Department of Logistics Management, Chongqing Three Gorges University, Chongqing, 404100, China
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27
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Allison SL, Jonaitis EM, Koscik RL, Hermann BP, Mueller KD, Cary RP, Ma Y, Rowley HA, Carlsson CM, Asthana S, Zetterberg H, Blennow K, Bendlin BB, Johnson SC. Neurodegeneration, Alzheimer's disease biomarkers, and longitudinal verbal learning and memory performance in late middle age. Neurobiol Aging 2021; 102:151-160. [PMID: 33765428 PMCID: PMC8286465 DOI: 10.1016/j.neurobiolaging.2021.01.030] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 12/29/2020] [Accepted: 01/27/2021] [Indexed: 12/12/2022]
Abstract
This study examined the effect of neurodegeneration, and its interaction with Alzheimer's disease (AD) cerebrospinal fluid biomarkers, on longitudinal verbal learning and memory performance in cognitively unimpaired (CU) late middle-aged adults. Three hundred and forty-two CU adults (cognitive baseline mean age = 58.4), with cerebrospinal fluid and structural MRI, completed 2-10 (median = 5) cognitive assessments. Learning and memory were assessed using the Rey Auditory Verbal Learning Test (RAVLT). We used sequential comparison of nested linear mixed effects models to analyze the data. Model selection preserved a significant ptau181/Aβ42 × global atrophy × age interaction; individuals with less global atrophy and lower ptau181/Aβ42 levels had less learning and delayed recall decline than individuals with more global atrophy and/or higher levels of ptau181/Aβ42. The hippocampal volume × age × ptau181/Aβ42 interaction was not significant. Findings suggest that in a sample of CU late middle-aged adults, individuals with AD biomarkers, global atrophy, or both evidence greater verbal learning and memory decline than individuals without either risk factor.
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Affiliation(s)
- Samantha L Allison
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Erin M Jonaitis
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Bruce P Hermann
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kimberly D Mueller
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Communication Sciences and Disorders, University of Wisconsin, Madison, WI, USA
| | - Robert P Cary
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Yue Ma
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Howard A Rowley
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Cynthia M Carlsson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sanjay Asthana
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Barbara B Bendlin
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
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28
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Mao C, Sha L, Li J, Huang X, Chu S, Lei D, Wang J, Dong L, Liu C, Xu Q, Peng B, Gao J. Relationship Between General Cognition, Visual Assessed Cortical Atrophy, and Cerebrospinal Fluid Biomarkers in Alzheimer's Disease: A Cross-Sectional Study from a Chinese PUMCH Cohort. J Alzheimers Dis 2021; 82:205-214. [PMID: 34024840 DOI: 10.3233/jad-210344] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) biomarkers are widely accepted as manifestations of Alzheimer's disease (AD) pathogenesis and incorporated into biological definition of AD. However, the correlations between CSF and other biomarkers such as neuroimaging and neuropsychiatric evaluation are complicated and inconsistent. OBJECTIVE We aimed to better interpreting CSF biomarkers results accompanying with other indexes in improving accurate diagnosis of AD. METHODS 112 AD patients and 30 cognitive normal controls were selected. Commercial accessible ELISA kits were introduced for measurement of CSF t-tau, p-tau181, Aβ1-42, and NfL based on standard protocol. MRI examinations were performed using a 3-T MRI scanner and visual rating scales including medial temporal atrophy score and Koedam's scale were used to evaluate medial temporal atrophy and posterior region atrophy. RESULTS CSF biomarkers' profile including decreased concentration of Aβ1-42, increased concentration of t-tau, p-tau181, t-tau/Aβ 1-42, and NfL were diagnostic between AD and control. CSF biomarkers profile was not influenced by the APOE genotype. Increased concentration of t-tau and NfL, as well as ratio of t-tau/Aβ 1-42 were related to decrease of Mini-Mental State Examination (MMSE) score while concentration of Aβ1-42 not. Visual assessed cortical atrophy was related to MMSE score, but most of the CSF biomarkers were not related to atrophy, except that increased concentration of p-tau181 was significantly associated with atrophy of posterior cortical region. CONCLUSION Our results supported CSF biomarkers were helpful in diagnosis of AD. However, CSF biomarkers were cross-sectional reflection of pathogenesis, which did not correlate well with clinical progression. CSF biomarkers should be interpreted in combination with MRI and cognitive evaluation in clinical use.
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Affiliation(s)
- Chenhui Mao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Longze Sha
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Jie Li
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Xinying Huang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Shanshan Chu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Dan Lei
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jie Wang
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Liling Dong
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Caiyan Liu
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Qi Xu
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Bin Peng
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
| | - Jing Gao
- Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science/Peking Union Medical College, Beijing, China
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Medina LD, Heffernan K, Holden S, Simpson A, Bettcher BM. Neural correlates of daily function: A pilot study of the white matter retrogenesis hypothesis and three separate performance-based functional assessments. Neuropsychology 2021; 35:103-110. [PMID: 33393804 DOI: 10.1037/neu0000649] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
OBJECTIVE Increasing evidence points to mild alterations in everyday functioning early in the course of Alzheimer's disease and related dementias (ADRD), despite prior research suggesting functional declines occur primarily in later stages. However, daily function assessment is typically accomplished with subjective self- or informant-report, which can be prone to error due to various factors. Performance-based functional assessments (PBFAs) allow for objective evaluation of daily function abilities, but little is known on their sensitivity to the earliest ADRD-related brain alterations. We aimed to determine the neural correlates of three different PBFAs in a pilot study. METHOD A total of 40 older participants (age = 70.9 ± 6.5 years; education = 17.0 ± 2.6 years; 51.5% female; 10.0% non-White; 67.5% cognitively normal) completed standardized PBFAs related to medication management (MM), finances (FIN), and communication abilities (COM). Participants underwent diffusion tensor imaging (DTI) scans, from which mean fractional anisotropy (FA) composite scores of late- (LMF) and early myelinated (EMF) fibers were calculated. Linear regression analyses controlling for age and global cognition were used to assess the relationship of PBFAs with FA. RESULTS Better performance on MM was associated with higher mean FA on LMF composite (t38 = 2.231, p = .032), while FIN and COM were not (ps > .05). PBFAs were not associated with EMF (p > .05). CONCLUSIONS Our preliminary findings demonstrate better performance on a PBFA of medication management is associated with higher FA in late-myelinated white matter tracts. Despite a small sample size, these results are consistent with growing evidence that performance-based functional assessments may be a useful tool in identifying early changes related to ADRD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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Affiliation(s)
| | - Kate Heffernan
- Department of Neurology, School of Medicine, University of Colorado
| | - Samantha Holden
- Department of Neurology, School of Medicine, University of Colorado
| | - Abigail Simpson
- Department of Neurology, School of Medicine, University of Colorado
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30
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2020 update on the clinical validity of cerebrospinal fluid amyloid, tau, and phospho-tau as biomarkers for Alzheimer's disease in the context of a structured 5-phase development framework. Eur J Nucl Med Mol Imaging 2021; 48:2121-2139. [PMID: 33674895 PMCID: PMC8175301 DOI: 10.1007/s00259-021-05258-7] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Accepted: 02/11/2021] [Indexed: 12/15/2022]
Abstract
Purpose In the last decade, the research community has focused on defining reliable biomarkers for the early detection of Alzheimer’s disease (AD) pathology. In 2017, the Geneva AD Biomarker Roadmap Initiative adapted a framework for the systematic validation of oncological biomarkers to cerebrospinal fluid (CSF) AD biomarkers—encompassing the 42 amino-acid isoform of amyloid-β (Aβ42), phosphorylated-tau (P-tau), and Total-tau (T-tau)—with the aim to accelerate their development and clinical implementation. The aim of this work is to update the current validation status of CSF AD biomarkers based on the Biomarker Roadmap methodology. Methods A panel of experts in AD biomarkers convened in November 2019 at a 2-day workshop in Geneva. The level of maturity (fully achieved, partly achieved, preliminary evidence, not achieved, unsuccessful) of CSF AD biomarkers was assessed based on the Biomarker Roadmap methodology before the meeting and presented and discussed during the workshop. Results By comparison to the previous 2017 Geneva Roadmap meeting, the primary advances in CSF AD biomarkers have been in the area of a unified protocol for CSF sampling, handling and storage, the introduction of certified reference methods and materials for Aβ42, and the introduction of fully automated assays. Additional advances have occurred in the form of defining thresholds for biomarker positivity and assessing the impact of covariates on their discriminatory ability. Conclusions Though much has been achieved for phases one through three, much work remains in phases four (real world performance) and five (assessment of impact/cost). To a large degree, this will depend on the availability of disease-modifying treatments for AD, given these will make accurate and generally available diagnostic tools key to initiate therapy. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05258-7.
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Massa F, Farotti L, Eusebi P, Capello E, Dottorini ME, Tranfaglia C, Bauckneht M, Morbelli S, Nobili F, Parnetti L. Reciprocal Incremental Value of 18F-FDG-PET and Cerebrospinal Fluid Biomarkers in Mild Cognitive Impairment Patients Suspected for Alzheimer's Disease and Inconclusive First Biomarker. J Alzheimers Dis 2020; 72:1193-1207. [PMID: 31683477 DOI: 10.3233/jad-190539] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND In Alzheimer's disease (AD) diagnosis, both cerebrospinal fluid (CSF) biomarkers and FDG-PET sometimes give inconclusive results. OBJECTIVE To evaluate the incremental diagnostic value of FDG-PET over CSF biomarkers, and vice versa, in patients with mild cognitive impairment (MCI) and suspected AD, in which the first biomarker resulted inconclusive. METHODS A consecutive series of MCI patients was retrospectively selected from two Memory Clinics where, as per clinical routine, either the first biomarker choice is FDG-PET and CSF biomarkers are only used in patients with uninformative FDG-PET, or vice versa. We defined criteria of uncertainty in interpretation of FDG-PET and CSF biomarkers, according to current evidence. The final diagnosis was established according to clinical-neuropsychological follow-up of at least one year (mean 4.4±2.2). RESULTS When CSF was used as second biomarker after FDG-PET, 14 out of 36 (39%) received informative results. Among these 14 patients, 11 (79%) were correctly classified with respect to final diagnosis, thus with a relative incremental value of CSF over FDG-PET of 30.6%. When FDG-PET was used as second biomarker, 26 out of 39 (67%) received informative results. Among these 26 patients, 15 (58%) were correctly classified by FDG-PET with respect to final diagnosis, thus with a relative incremental value over CSF of 38.5%. CONCLUSION Our real-world data confirm the added values of FDG-PET (or CSF) in a diagnostic pathway where CSF (or FDG-PET) was used as first biomarkers in suspected AD. These findings should be replicated in larger studies with prospective enrolment according to a Phase III design.
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Affiliation(s)
- Federico Massa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy
| | - Lucia Farotti
- Center for Memory Disorders and Laboratory of Clinical Neurochemistry, Neurology Clinic, University of Perugia, Perugia, Italy
| | - Paolo Eusebi
- Section of Neurology, Department of Medicine, University of Perugia, Perugia, Italy.,Health Planning Service, Department of Epidemiology, Regional Health Authority of Umbria, Perugia, Italy
| | | | - Massimo E Dottorini
- Nuclear Medicine Unit, "S. Maria della Misericordia" Hospital, Perugia, Italy
| | - Cristina Tranfaglia
- Nuclear Medicine Unit, "S. Maria della Misericordia" Hospital, Perugia, Italy
| | - Matteo Bauckneht
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Silvia Morbelli
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy
| | - Flavio Nobili
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health, University of Genoa, Genoa, Italy.,Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Lucilla Parnetti
- Center for Memory Disorders and Laboratory of Clinical Neurochemistry, Neurology Clinic, University of Perugia, Perugia, Italy
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Busatto Filho G, Duran FLDS, Squarzoni P, Coutinho AMN, Rosa PGP, Torralbo L, Pachi CGDF, da Costa NA, Porto FHDG, Carvalho CL, Brucki SMD, Nitrini R, Forlenza OV, Leite CDC, Buchpiguel CA, de Paula Faria D. Hippocampal subregional volume changes in elders classified using positron emission tomography-based Alzheimer's biomarkers of β-amyloid deposition and neurodegeneration. J Neurosci Res 2020; 99:481-501. [PMID: 33073383 DOI: 10.1002/jnr.24739] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 09/16/2020] [Accepted: 09/30/2020] [Indexed: 12/14/2022]
Abstract
Changes in hippocampal subfield volumes (HSV) along the Alzheimer's disease (AD) continuum have been scarcely investigated to date in elderly subjects classified based on the presence of β-amyloid aggregation and signs of neurodegeneration. We classified patients (either sex) with mild dementia compatible with AD (n = 35) or amnestic mild cognitive impairment (n = 39), and cognitively unimpaired subjects (either sex; n = 26) using [11 C]PIB-PET to assess β-amyloid aggregation (A+) and [18 F]FDG-PET to account for neurodegeneration ((N)+). Magnetic resonance imaging-based automated methods were used for HSV and white matter hyperintensity (WMH) measurements. Significant HSV reductions were found in A+(N)+ subjects in the presubiculum/subiculum complex and molecular layer, related to worse memory performance. In both the A+(N)+ and A+(N)- categories, subicular volumes were inversely correlated with the degree of Aβ deposition. The A-(N)+ subgroup showed reduced HSV relative to the A-(N)- subgroup also in the subiculum/presubiculum. Combining all (N)- subjects, HSV were lower in subjects presenting significant cognitive decline irrespective of A+/A- classification (controlling for WMH load); these between-group differences were detected again in the presubiculum, but also involved the CA4 and granular layer. These findings demonstrate that differential HSV reductions are detectable both in (N)+ and (N)- categories along the AD continuum, and are directly related to the severity of cognitive deficits. HSV reductions are larger both in A+(N)+ and A+(N)- subjects in direct proportion to the degree of Aβ deposition. The meaningful HSV reductions detected in the A-(N)+ subgroup highlights the strength of biomarker-based classifications outside of the classical AD continuum.
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Affiliation(s)
- Geraldo Busatto Filho
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Fabio Luiz de Souza Duran
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Paula Squarzoni
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Artur Martins Novaes Coutinho
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Pedro Gomes Penteado Rosa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Leticia Torralbo
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Clarice Gameiro da Fonseca Pachi
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Naomi Antunes da Costa
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Fabio Henrique de Gobbi Porto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Cleudiana Lima Carvalho
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Sonia Maria Dozzi Brucki
- Department of Neurology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Ricardo Nitrini
- Department of Neurology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Orestes Vicente Forlenza
- Laboratory of Neuroscience (LIM 27), Department of Psychiatry, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Claudia da Costa Leite
- Laboratory of Magnetic Resonance in Neuroradiology (LIM44), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Carlos Alberto Buchpiguel
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
| | - Daniele de Paula Faria
- Laboratory of Nuclear Medicine (LIM43), Department of Radiology and Oncology, Faculdade de Medicina FMUSP, Universidade de Sao Paulo, Sao Paulo, Brazil
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Pillai JA, Bonner-Jackson A, Bekris LM, Safar J, Bena J, Leverenz JB. Highly Elevated Cerebrospinal Fluid Total Tau Level Reflects Higher Likelihood of Non-Amnestic Subtype of Alzheimer's Disease. J Alzheimers Dis 2020; 70:1051-1058. [PMID: 31306137 DOI: 10.3233/jad-190519] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
BACKGROUND Cerebrospinal fluid (CSF) levels of total tau (t-tau) protein are thought to reflect the intensity of the neuronal damage in neurodegeneration, including Alzheimer's disease (AD). The recent link of CSF t-tau to rapidly progressive AD raises the question among other AD clinical variants regarding CSF t-tau. We investigated the clinical phenotypes of AD patients with varying CSF t-tau levels. OBJECTIVE We tested the hypothesis that highly elevated CSF t-tau level would have a higher likelihood of presenting with atypical non-amnestic variants of AD. METHODS Retrospective comparative case study of 97 patients evaluated in a memory clinic with clinical presentation and CSF biomarkers consistent with AD. We compared the age, sex, education, APOEɛ4 status, Montreal Cognitive Assessment (MoCA) score, clinical phenotype, and MRI volumetric measures by CSF t-tau quartile at baseline. Multivariable logistic regression models were used to evaluate if CSF t-tau levels predict non-amnestic presentations controlling for covariates. RESULTS Non-amnestic AD had a higher median CSF t-tau level compared to amnestic-AD (p = 0.014). Each 50 pg/ml increase in CSF t-tau was associated with an increase in the odds of having a non-amnestic presentation (7.4%) and aphasia (10.6 %) as the initial presenting symptom even after taking into account; age, sex, education, APOEɛ4, MoCA, and CSF Aβ42. Logopenic AD had higher t-tau and p-tau levels compared to other variants. CONCLUSIONS Highly elevated CSF t-tau levels could indicate more cortical involvement presenting with early non-amnestic symptoms in atypical AD subtypes, particularly in the logopenic variant.
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Affiliation(s)
- Jagan A Pillai
- Lou Ruvo Center for Brain Health, Cleveland Clinic, Cleveland, OH, USA.,Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
| | | | - Lynn M Bekris
- Department of Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jiri Safar
- Department of Pathology, Cleveland, OH, USA.,Department of University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Jim Bena
- Department of Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Cleveland Clinic, Cleveland, OH, USA.,Department of Neurology, Cleveland Clinic, Cleveland, OH, USA
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Cavedoni S, Chirico A, Pedroli E, Cipresso P, Riva G. Digital Biomarkers for the Early Detection of Mild Cognitive Impairment: Artificial Intelligence Meets Virtual Reality. Front Hum Neurosci 2020; 14:245. [PMID: 32848660 PMCID: PMC7396670 DOI: 10.3389/fnhum.2020.00245] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Accepted: 06/02/2020] [Indexed: 01/16/2023] Open
Abstract
Elderly people affected by Mild Cognitive Impairment (MCI) usually report a perceived decline in cognitive functions that deeply impacts their quality of life. This subtle waning, although it cannot be diagnosable as dementia, is noted by caregivers on the basis of their relative’s behaviors. Crucially, if this condition is also not detected in time by clinicians, it can easily turn into dementia. Thus, early detection of MCI is strongly needed. Classical neuropsychological measures – underlying a categorical model of diagnosis - could be integrated with a dimensional assessment approach involving Virtual Reality (VR) and Artificial Intelligence (AI). VR can be used to create highly ecologically controlled simulations resembling the daily life contexts in which patients’ daily instrumental activities (IADL) usually take place. Clinicians can record patients’ kinematics, particularly gait, while performing IADL (Digital Biomarkers). Then, Artificial Intelligence employs Machine Learning (ML) to analyze them in combination with clinical and neuropsychological data. This integrated computational approach would enable the creation of a predictive model to identify specific patterns of cognitive and motor impairment in MCI. Therefore, this new dimensional cognitive-behavioral assessment would reveal elderly people’s neural alterations and impaired cognitive functions, typical of MCI and dementia, even in early stages for more time-sensitive interventions.
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Affiliation(s)
- Silvia Cavedoni
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy
| | - Alice Chirico
- Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Elisa Pedroli
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Faculty of Psychology, eCampus University, Novedrate, Italy
| | - Pietro Cipresso
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
| | - Giuseppe Riva
- Applied Technology for Neuro-Psychology Lab, Istituto Auxologico Italiano, Milan, Italy.,Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
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35
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Predicting Alzheimer's disease progression using deep recurrent neural networks. Neuroimage 2020; 222:117203. [PMID: 32763427 PMCID: PMC7797176 DOI: 10.1016/j.neuroimage.2020.117203] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2019] [Revised: 07/22/2020] [Accepted: 07/23/2020] [Indexed: 01/12/2023] Open
Abstract
Early identification of individuals at risk of developing Alzheimer’s disease (AD) dementia is important for developing disease-modifying therapies. In this study, given multimodal AD markers and clinical diagnosis of an individual from one or more timepoints, we seek to predict the clinical diagnosis, cognition and ventricular volume of the individual for every month (indefinitely) into the future. We proposed and applied a minimal recurrent neural network (minimalRNN) model to data from The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge, comprising longitudinal data of 1677 participants (Marinescu et al., 2018) from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We compared the performance of the minimalRNN model and four baseline algorithms up to 6 years into the future. Most previous work on predicting AD progression ignore the issue of missing data, which is a prevalent issue in longitudinal data. Here, we explored three different strategies to handle missing data. Two of the strategies treated the missing data as a “preprocessing” issue, by imputing the missing data using the previous timepoint (“forward filling”) or linear interpolation (“linear filling). The third strategy utilized the minimalRNN model itself to fill in the missing data both during training and testing (“model filling”). Our analyses suggest that the minimalRNN with “model filling” compared favorably with baseline algorithms, including support vector machine/regression, linear state space (LSS) model, and long short-term memory (LSTM) model. Importantly, although the training procedure utilized longitudinal data, we found that the trained minimalRNN model exhibited similar performance, when using only 1 input timepoint or 4 input timepoints, suggesting that our approach might work well with just cross-sectional data. An earlier version of our approach was ranked 5th (out of 53 entries) in the TADPOLE challenge in 2019. The current approach is ranked 2nd out of 63 entries as of June 3rd, 2020.
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36
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Casamitjana A, Petrone P, Molinuevo JL, Gispert JD, Vilaplana V. Projection to Latent Spaces Disentangles Pathological Effects on Brain Morphology in the Asymptomatic Phase of Alzheimer's Disease. Front Neurol 2020; 11:648. [PMID: 32849173 PMCID: PMC7399334 DOI: 10.3389/fneur.2020.00648] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2019] [Accepted: 06/02/2020] [Indexed: 01/14/2023] Open
Abstract
Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.
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Affiliation(s)
- Adrià Casamitjana
- Image and Video Processing Unit, Department of Signal Theory and Communications, UPCBarcelona Tech, Barcelona, Spain
| | - Paula Petrone
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain
| | - José Luis Molinuevo
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain.,Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain
| | - Juan Domingo Gispert
- Barcelonabeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain.,Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain.,CIBER de Bioengeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Verónica Vilaplana
- Image and Video Processing Unit, Department of Signal Theory and Communications, UPCBarcelona Tech, Barcelona, Spain
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37
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Devanarayan P, Devanarayan V, Llano DA. Identification of a Simple and Novel Cut-Point Based Cerebrospinal Fluid and MRI Signature for Predicting Alzheimer's Disease Progression that Reinforces the 2018 NIA-AA Research Framework. J Alzheimers Dis 2020; 68:537-550. [PMID: 30775985 DOI: 10.3233/jad-180905] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The 2018 NIA-AA research framework proposes a classification system with Amyloid-β deposition, pathologic Tau, and Neurodegeneration (ATN) for diagnosis and staging of Alzheimer's disease (AD). Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database can be utilized to identify diagnostic signatures for predicting AD progression, and to determine the utility of this NIA-AA research framework. Profiles of 320 peptides from baseline cerebrospinal fluid (CSF) samples of 287 normal, mild cognitive impairment (MCI), and AD subjects followed over a 3-10-year period were measured via multiple reaction monitoring mass spectrometry. CSF Aβ42, total-Tau (tTau), phosphorylated-Tau (pTau-181), and hippocampal volume were also measured. From these candidate markers, optimal signatures with decision thresholds to separate AD and normal subjects were first identified via unbiased regression and tree-based algorithms. The best performing signature determined via cross-validation was then tested in an independent group of MCI subjects to predict future progression. This multivariate analysis yielded a simple diagnostic signature comprising CSF pTau-181 to Aβ42 ratio, MRI hippocampal volume, and low CSF levels of a novel PTPRN peptide, with a decision threshold on each marker. When applied to a separate MCI group at baseline, subjects meeting these signature criteria experience 4.3-fold faster progression to AD compared to a 2.2-fold faster progression using only conventional markers. This novel 4-marker signature represents an advance over the current diagnostics based on widely used markers, and is easier to use in practice than recently published complex signatures. This signature also reinforces the ATN construct from the 2018 NIA-AA research framework.
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Affiliation(s)
| | - Viswanath Devanarayan
- Charles River Laboratories, Horsham, PA, USA.,Department of Mathematics, Statistics and Computer Science, University of Illinois at Chicago, IL, USA
| | - Daniel A Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA.,Carle Neuroscience Institute, Urbana, IL, USA
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38
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Banning LCP, Ramakers IHGB, Köhler S, Bron EE, Verhey FRJ, de Deyn PP, Claassen JAHR, Koek HL, Middelkoop HAM, van der Flier WM, van der Lugt A, Aalten P. The Association Between Biomarkers and Neuropsychiatric Symptoms Across the Alzheimer's Disease Spectrum. Am J Geriatr Psychiatry 2020; 28:735-744. [PMID: 32088096 DOI: 10.1016/j.jagp.2020.01.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 01/17/2020] [Accepted: 01/21/2020] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To investigate the relationship between Alzheimer's disease biomarkers and neuropsychiatric symptoms. METHODS Data from two large cohort studies, the Dutch Parelsnoer Institute - Neurodegenerative Diseases and the Alzheimer's Disease Neuroimaging Initiative was used, including subjects with subjective cognitive decline (N = 650), mild cognitive impairment (N = 887), and Alzheimer's disease dementia (N = 626). Cerebrospinal fluid (CSF) levels of Aβ42, t-tau, p-tau, and hippocampal volume were associated with neuropsychiatric symptoms (measured with the Neuropsychiatric Inventory) using multiple logistic regression analyses. The effect of the Mini-Mental State Examination (as proxy for cognitive functioning) on these relationships was assessed with mediation analyses. RESULTS Alzheimer's disease biomarkers were not associated with depression, agitation, irritability, and sleep disturbances. Lower levels of CSF Aβ42, higher levels of t- and p-tau were associated with presence of anxiety. Lower levels of CSF Aβ42 and smaller hippocampal volumes were associated with presence of apathy. All associations were mediated by cognitive functioning. CONCLUSION The association between Alzheimer's disease pathology and anxiety and apathy is partly due to impairment in cognitive functioning.
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Affiliation(s)
- Leonie C P Banning
- Department of Psychiatry and Neuropsychology (LCPB, IHGBR, SK, FRJV, PA), Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, the Netherlands
| | - Inez H G B Ramakers
- Department of Psychiatry and Neuropsychology (LCPB, IHGBR, SK, FRJV, PA), Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, the Netherlands.
| | - Sebastian Köhler
- Department of Psychiatry and Neuropsychology (LCPB, IHGBR, SK, FRJV, PA), Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, the Netherlands
| | - Esther E Bron
- Departments of Radiology and Nuclear Medicine (EEB, AVDL), Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - Frans R J Verhey
- Department of Psychiatry and Neuropsychology (LCPB, IHGBR, SK, FRJV, PA), Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, the Netherlands
| | - Peter Paul de Deyn
- Department of Neurology (PPDD), Alzheimer Center, University of Groningen, University Medical Center, Groningen, the Netherlands
| | - Jurgen A H R Claassen
- Department of Geriatric Medicine (JAHRC), Radboudumc Alzheimer Center, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Huiberdina L Koek
- Department of Geriatrics (HLK), University Medical Center Utrecht, Utrecht, the Netherlands
| | - Huub A M Middelkoop
- Department of Neurology and Neuropsychology (HAMM), Leiden University Medical Center, Leiden, the Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, VU University Medical Center (WMVDF), Amsterdam, the Netherlands
| | - Aad van der Lugt
- Departments of Radiology and Nuclear Medicine (EEB, AVDL), Erasmus MC - University Medical Center, Rotterdam, the Netherlands
| | - Pauline Aalten
- Department of Psychiatry and Neuropsychology (LCPB, IHGBR, SK, FRJV, PA), Maastricht University, School for Mental Health and Neuroscience, Alzheimer Center Limburg, Maastricht, the Netherlands
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39
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Prediction of Cognitive Decline in Temporal Lobe Epilepsy and Mild Cognitive Impairment by EEG, MRI, and Neuropsychology. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2020; 2020:8915961. [PMID: 32549888 PMCID: PMC7256687 DOI: 10.1155/2020/8915961] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 05/06/2020] [Indexed: 12/20/2022]
Abstract
Cognitive decline is a severe concern of patients with mild cognitive impairment. Also, in patients with temporal lobe epilepsy, memory problems are a frequently encountered problem with potential progression. On the background of a unifying hypothesis for cognitive decline, we merged knowledge from dementia and epilepsy research in order to identify biomarkers with a high predictive value for cognitive decline across and beyond these groups that can be fed into intelligent systems. We prospectively assessed patients with temporal lobe epilepsy (N = 9), mild cognitive impairment (N = 19), and subjective cognitive complaints (N = 4) and healthy controls (N = 18). All had structural cerebral MRI, EEG at rest and during declarative verbal memory performance, and a neuropsychological assessment which was repeated after 18 months. Cognitive decline was defined as significant change on neuropsychological subscales. We extracted volumetric and shape features from MRI and brain network measures from EEG and fed these features alongside a baseline testing in neuropsychology into a machine learning framework with feature subset selection and 5-fold cross validation. Out of 50 patients, 27 had a decline over time in executive functions, 23 in visual-verbal memory, 23 in divided attention, and 7 patients had an increase in depression scores. The best sensitivity/specificity for decline was 72%/82% for executive functions based on a feature combination from MRI volumetry and EEG partial coherence during recall of memories; 95%/74% for visual-verbal memory by combination of MRI-wavelet features and neuropsychology; 84%/76% for divided attention by combination of MRI-wavelet features and neuropsychology; and 81%/90% for increase of depression by combination of EEG partial directed coherence factor at rest and neuropsychology. Combining information from EEG, MRI, and neuropsychology in order to predict neuropsychological changes in a heterogeneous population could create a more general model of cognitive performance decline.
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40
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Van Egroo M, Narbutas J, Chylinski D, Villar González P, Maquet P, Salmon E, Bastin C, Collette F, Vandewalle G. Sleep-wake regulation and the hallmarks of the pathogenesis of Alzheimer's disease. Sleep 2020; 42:5289316. [PMID: 30649520 DOI: 10.1093/sleep/zsz017] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/26/2018] [Indexed: 01/23/2023] Open
Abstract
While efficient treatments for Alzheimer's disease (AD) remain elusive, a growing body of research has highlighted sleep-wake regulation as a potential modifiable factor to delay disease progression. Evidence accumulated in recent years is pointing toward a tight link between sleep-wake disruption and the three main hallmarks of the pathogenesis of AD, i.e. abnormal amyloid-beta (Aβ) and tau proteins accumulation, and neurodegeneration. However, all three hallmarks are rarely considered together in the same study. In this review, we gather and discuss findings in favor of an association between sleep-wake disruption and each AD hallmark in animal models and in humans, with a focus on the preclinical stages of the disease. We emphasize that these relationships are likely bidirectional for each of these hallmarks. Altogether, current findings provide strong support for considering sleep-wake disruption as a true risk factor in the early unfolding of AD, but more research integrating recent technical advances is needed, particularly with respect to tau protein and neurodegeneration. Interventional longitudinal studies among cognitively healthy older individuals should assess the practical use of improving sleep-wake regulation to slow down the progression of AD pathogenesis.
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Affiliation(s)
- Maxime Van Egroo
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | - Justinas Narbutas
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Daphne Chylinski
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
| | | | - Pierre Maquet
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Eric Salmon
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium.,Department of Neurology, University Hospital of Liège, Liège, Belgium
| | - Christine Bastin
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Fabienne Collette
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium.,Psychology and Cognitive Neuroscience Research Unit, University of Liège, Liège, Belgium
| | - Gilles Vandewalle
- GIGA-Cyclotron Research Centre-In Vivo Imaging, University of Liège, Liège, Belgium
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Lombardi G, Crescioli G, Cavedo E, Lucenteforte E, Casazza G, Bellatorre A, Lista C, Costantino G, Frisoni G, Virgili G, Filippini G. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment. Cochrane Database Syst Rev 2020; 3:CD009628. [PMID: 32119112 PMCID: PMC7059964 DOI: 10.1002/14651858.cd009628.pub2] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) due to Alzheimer's disease is the symptomatic predementia phase of Alzheimer's disease dementia, characterised by cognitive and functional impairment not severe enough to fulfil the criteria for dementia. In clinical samples, people with amnestic MCI are at high risk of developing Alzheimer's disease dementia, with annual rates of progression from MCI to Alzheimer's disease estimated at approximately 10% to 15% compared with the base incidence rates of Alzheimer's disease dementia of 1% to 2% per year. OBJECTIVES To assess the diagnostic accuracy of structural magnetic resonance imaging (MRI) for the early diagnosis of dementia due to Alzheimer's disease in people with MCI versus the clinical follow-up diagnosis of Alzheimer's disease dementia as a reference standard (delayed verification). To investigate sources of heterogeneity in accuracy, such as the use of qualitative visual assessment or quantitative volumetric measurements, including manual or automatic (MRI) techniques, or the length of follow-up, and age of participants. MRI was evaluated as an add-on test in addition to clinical diagnosis of MCI to improve early diagnosis of dementia due to Alzheimer's disease in people with MCI. SEARCH METHODS On 29 January 2019 we searched Cochrane Dementia and Cognitive Improvement's Specialised Register and the databases, MEDLINE, Embase, BIOSIS Previews, Science Citation Index, PsycINFO, and LILACS. We also searched the reference lists of all eligible studies identified by the electronic searches. SELECTION CRITERIA We considered cohort studies of any size that included prospectively recruited people of any age with a diagnosis of MCI. We included studies that compared the diagnostic test accuracy of baseline structural MRI versus the clinical follow-up diagnosis of Alzheimer's disease dementia (delayed verification). We did not exclude studies on the basis of length of follow-up. We included studies that used either qualitative visual assessment or quantitative volumetric measurements of MRI to detect atrophy in the whole brain or in specific brain regions, such as the hippocampus, medial temporal lobe, lateral ventricles, entorhinal cortex, medial temporal gyrus, lateral temporal lobe, amygdala, and cortical grey matter. DATA COLLECTION AND ANALYSIS Four teams of two review authors each independently reviewed titles and abstracts of articles identified by the search strategy. Two teams of two review authors each independently assessed the selected full-text articles for eligibility, extracted data and solved disagreements by consensus. Two review authors independently assessed the quality of studies using the QUADAS-2 tool. We used the hierarchical summary receiver operating characteristic (HSROC) model to fit summary ROC curves and to obtain overall measures of relative accuracy in subgroup analyses. We also used these models to obtain pooled estimates of sensitivity and specificity when sufficient data sets were available. MAIN RESULTS We included 33 studies, published from 1999 to 2019, with 3935 participants of whom 1341 (34%) progressed to Alzheimer's disease dementia and 2594 (66%) did not. Of the participants who did not progress to Alzheimer's disease dementia, 2561 (99%) remained stable MCI and 33 (1%) progressed to other types of dementia. The median proportion of women was 53% and the mean age of participants ranged from 63 to 87 years (median 73 years). The mean length of clinical follow-up ranged from 1 to 7.6 years (median 2 years). Most studies were of poor methodological quality due to risk of bias for participant selection or the index test, or both. Most of the included studies reported data on the volume of the total hippocampus (pooled mean sensitivity 0.73 (95% confidence interval (CI) 0.64 to 0.80); pooled mean specificity 0.71 (95% CI 0.65 to 0.77); 22 studies, 2209 participants). This evidence was of low certainty due to risk of bias and inconsistency. Seven studies reported data on the atrophy of the medial temporal lobe (mean sensitivity 0.64 (95% CI 0.53 to 0.73); mean specificity 0.65 (95% CI 0.51 to 0.76); 1077 participants) and five studies on the volume of the lateral ventricles (mean sensitivity 0.57 (95% CI 0.49 to 0.65); mean specificity 0.64 (95% CI 0.59 to 0.70); 1077 participants). This evidence was of moderate certainty due to risk of bias. Four studies with 529 participants analysed the volume of the total entorhinal cortex and four studies with 424 participants analysed the volume of the whole brain. We did not estimate pooled sensitivity and specificity for the volume of these two regions because available data were sparse and heterogeneous. We could not statistically evaluate the volumes of the lateral temporal lobe, amygdala, medial temporal gyrus, or cortical grey matter assessed in small individual studies. We found no evidence of a difference between studies in the accuracy of the total hippocampal volume with regards to duration of follow-up or age of participants, but the manual MRI technique was superior to automatic techniques in mixed (mostly indirect) comparisons. We did not assess the relative accuracy of the volumes of different brain regions measured by MRI because only indirect comparisons were available, studies were heterogeneous, and the overall accuracy of all regions was moderate. AUTHORS' CONCLUSIONS The volume of hippocampus or medial temporal lobe, the most studied brain regions, showed low sensitivity and specificity and did not qualify structural MRI as a stand-alone add-on test for an early diagnosis of dementia due to Alzheimer's disease in people with MCI. This is consistent with international guidelines, which recommend imaging to exclude non-degenerative or surgical causes of cognitive impairment and not to diagnose dementia due to Alzheimer's disease. In view of the low quality of most of the included studies, the findings of this review should be interpreted with caution. Future research should not focus on a single biomarker, but rather on combinations of biomarkers to improve an early diagnosis of Alzheimer's disease dementia.
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Affiliation(s)
- Gemma Lombardi
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Giada Crescioli
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Enrica Cavedo
- Pitie‐Salpetriere Hospital, Sorbonne UniversityAlzheimer Precision Medicine (APM), AP‐HP47 boulevard de l'HopitalParisFrance75013
| | - Ersilia Lucenteforte
- University of PisaDepartment of Clinical and Experimental MedicineVia Savi 10PisaItaly56126
| | - Giovanni Casazza
- Università degli Studi di MilanoDipartimento di Scienze Biomediche e Cliniche "L. Sacco"via GB Grassi 74MilanItaly20157
| | | | - Chiara Lista
- Fondazione I.R.C.C.S. Istituto Neurologico Carlo BestaNeuroepidemiology UnitVia Celoria, 11MilanoItaly20133
| | - Giorgio Costantino
- Ospedale Maggiore Policlinico, Università degli Studi di MilanoUOC Pronto Soccorso e Medicina D'Urgenza, Fondazione IRCCS Ca' GrandaMilanItaly
| | | | - Gianni Virgili
- University of FlorenceDepartment of Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA)Largo Brambilla, 3FlorenceItaly50134
| | - Graziella Filippini
- Carlo Besta Foundation and Neurological InstituteScientific Director’s Officevia Celoria, 11MilanItaly20133
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Balsis S, Geraci L, Benge J, Lowe DA, Choudhury TK, Tirso R, Doody RS. Statistical Model of Dynamic Markers of the Alzheimer's Pathological Cascade. J Gerontol B Psychol Sci Soc Sci 2019; 73:964-973. [PMID: 29741663 DOI: 10.1093/geronb/gbx156] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2017] [Accepted: 02/27/2018] [Indexed: 11/12/2022] Open
Abstract
Objectives Alzheimer's disease (AD) is a progressive disease reflected in markers across assessment modalities, including neuroimaging, cognitive testing, and evaluation of adaptive function. Identifying a single continuum of decline across assessment modalities in a single sample is statistically challenging because of the multivariate nature of the data. To address this challenge, we implemented advanced statistical analyses designed specifically to model complex data across a single continuum. Method We analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI; N = 1,056), focusing on indicators from the assessments of magnetic resonance imaging (MRI) volume, fluorodeoxyglucose positron emission tomography (FDG-PET) metabolic activity, cognitive performance, and adaptive function. Item response theory was used to identify the continuum of decline. Then, through a process of statistical scaling, indicators across all modalities were linked to that continuum and analyzed. Results Findings revealed that measures of MRI volume, FDG-PET metabolic activity, and adaptive function added measurement precision beyond that provided by cognitive measures, particularly in the relatively mild range of disease severity. More specifically, MRI volume, and FDG-PET metabolic activity become compromised in the very mild range of severity, followed by cognitive performance and finally adaptive function. Conclusion Our statistically derived models of the AD pathological cascade are consistent with existing theoretical models.
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Affiliation(s)
- Steve Balsis
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Lisa Geraci
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Jared Benge
- Baylor Scott and White Neurosciences Institute, Temple, Texas
| | - Deborah A Lowe
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Tabina K Choudhury
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Robert Tirso
- Department of Psychological and Brain Sciences, Texas A&M University, College Station
| | - Rachelle S Doody
- Department of Neurology, Baylor College of Medicine, Alzheimer's Disease and Memory Disorders Center, Houston, Texas
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Casamitjana A, Petrone P, Molinuevo JL, Gispert JD, Vilaplana V. Shared Latent Structures Between Imaging Features and Biomarkers in Early Stages of Alzheimer's Disease: A Predictive Study. IEEE J Biomed Health Inform 2019; 24:365-376. [PMID: 31380776 DOI: 10.1109/jbhi.2019.2932565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Magnetic resonance imaging (MRI) provides high resolution brain morphological information and is used as a biomarker in neurodegenerative diseases. Population studies of brain morphology often seek to identify pathological structural changes related to different diagnostic categories (e.g.: controls, mild cognitive impairment or dementia) which normally describe highly heterogeneous groups with a single categorical variable. Instead, multiple biomarkers are used as a proxy for pathology and are more powerful in capturing structural variability. Hence, using the joint modeling of brain morphology and biomarkers, we aim at describing structural changes related to any brain condition by means of few underlying processes. In this regard, we use a multivariate approach based on Projection to Latent Structures in its regression variant (PLSR) to study structural changes related to aging and AD pathology. MRI volumetric and cortical thickness measurements are used for brain morphology and cerebrospinal fluid (CSF) biomarkers (t-tau, p-tau and amyloid-beta) are used as a proxy for AD pathology. By relating both sets of measurements, PLSR finds a low-dimensional latent space describing AD pathological effects on brain structure. The proposed framework allows us to separately model aging effects on brain morphology as a confounder variable orthogonal to the pathological effect. The predictive power of the associated latent spaces (i.e., the capacity of predicting biomarker values) is assessed in a cross-validation framework.
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Allison SL, Koscik RL, Cary RP, Jonaitis EM, Rowley HA, Chin NA, Zetterberg H, Blennow K, Carlsson CM, Asthana S, Bendlin BB, Johnson SC. Comparison of different MRI-based morphometric estimates for defining neurodegeneration across the Alzheimer's disease continuum. NEUROIMAGE-CLINICAL 2019; 23:101895. [PMID: 31252287 PMCID: PMC6599872 DOI: 10.1016/j.nicl.2019.101895] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 05/31/2019] [Accepted: 06/09/2019] [Indexed: 01/05/2023]
Abstract
Background Several neurodegeneration (N) metrics using structural MRI are used for the purpose of Alzheimer's disease (AD)-related staging, including hippocampal volume, global atrophy, and an “AD signature” composite consisting of thickness or volumetric estimates derived from regions impacted early in AD. This study sought to determine if less user-intensive estimates of global atrophy and hippocampal volume were equivalent to a thickness-based AD signature from FreeSurfer for defining N across the AD continuum (i.e., individuals who are amyloid-positive (A+)). Methods Cognitively unimpaired (CU) late middle-aged and older adults, as well as A+ mild cognitive impairment (MCI) and A+ AD dementia individuals, with available CSF and structural MRI scan <1.5 years apart, were selected for the study (n = 325, mean age = 62). First, in a subsample of A+ AD dementia and matched biomarker-negative (i.e., A- and tau tangle pathology (T)-) CU controls (n = 40), we examined ROC characteristics and identified N cut-offs using Youden's J for neurofilament light chain protein (NfL) and each of three MRI-based measures: a thickness-based AD signature from FreeSurfer, hippocampal volume (using FIRST), and a simple estimate of global atrophy (the ratio of intracranial CSF segmented volume to brain tissue volume, using SPM12). Based on the results from the ROC analyses, we then examined the concordance between NfL N positivity and N positivity for each MRI-based metric using Cohen's Kappa in the remaining subsample of 285 individuals. Finally, in the full sample (n = 325), we examined the relationship between the four measures of N and group membership across the AD continuum using Kruskal-Wallis tests and Cliff's deltas. Results The three MRI-based metrics and CSF NfL similarly discriminated between the A-T- CU (n = 20) and A+ AD (n = 20) groups (AUCs ≥0.885; ps < 0.001). Using the cut-off values derived from the ROCs to define N positivity, there was weak concordance between NfL and all three MRI-derived metrics of N in the subsample of 285 individuals (Cohen's Kappas ≤0.429). Finally, the three MRI-based measures of N and CSF NfL showed similar associations with AD continuum group (i.e., Kruskal-Wallis ps < 0.001), with relatively larger effect sizes noted when comparing the A-T- CU to the A+ MCI (Cliff's deltas ≥0.741) and A+ AD groups (Cliff's deltas ≥0.810) than to the A+T- CU (Cliff's deltas = 0.112–0.298) and A + T+ CU groups (Cliff's deltas = 0.212–0.731). Conclusions These findings suggest that the three MRI-based morphometric estimates and CSF NfL similarly differentiate individuals across the AD continuum on N status. In many applications, a simple estimate of global atrophy may be preferred as an MRI marker of N across the AD continuum given its methodological robustness and ease of calculation when compared to hippocampal volume or a cortical thickness AD signature. Hippocampus, global atrophy and AD signature had strong diagnostic accuracy in AD. There was weak concordance between NfL and all three MRI-derived metrics. NfL and the three MRI-derived metrics performed similarly across the AD spectrum.
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Affiliation(s)
- Samantha L Allison
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Rebecca L Koscik
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Robert P Cary
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Erin M Jonaitis
- Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Howard A Rowley
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Nathaniel A Chin
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Institute of Neurology, University College London, London, UK; UK Dementia Research Institute at UCL, London, UK
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Cynthia M Carlsson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Sanjay Asthana
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Barbara B Bendlin
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Sterling C Johnson
- Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Wisconsin Alzheimer's Institute, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA.
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Wang M, Zhang D, Shen D, Liu M. Multi-task exclusive relationship learning for alzheimer's disease progression prediction with longitudinal data. Med Image Anal 2019; 53:111-122. [PMID: 30763830 PMCID: PMC6397780 DOI: 10.1016/j.media.2019.01.007] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2018] [Revised: 01/21/2019] [Accepted: 01/26/2019] [Indexed: 11/23/2022]
Abstract
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Currently, many multi-task learning approaches have been proposed to predict the disease progression at the early stage using longitudinal data, with each task corresponding to a particular time point. However, the underlying association among different time points in disease progression is still under-explored in previous studies. To this end, we propose a multi-task exclusive relationship learning model to automatically capture the intrinsic relationship among tasks at different time points for estimating clinical measures based on longitudinal imaging data. The proposed method can select the most discriminative features for different tasks and also model the intrinsic relatedness among different time points, by utilizing an exclusive lasso regularization and a relationship induced regularization. Specifically, the exclusive lasso regularization enables partial group structure feature selection among the longitudinal data, while the relationship induced regularization efficiently introduces the relationship information from data to guide knowledge transfer. We further develop an efficient optimization algorithm to solve the proposed objective function. Extensive experiments on both synthetic and real datasets demonstrate the effectiveness of our proposed method. In comparison with several state-of-the-art methods, our proposed method can achieve promising performance for cognitive status prediction and also can help discover disease-related biomarkers.
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Affiliation(s)
- Mingliang Wang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
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Fede SJ, Grodin EN, Dean SF, Diazgranados N, Momenan R. Resting state connectivity best predicts alcohol use severity in moderate to heavy alcohol users. Neuroimage Clin 2019; 22:101782. [PMID: 30921611 PMCID: PMC6438989 DOI: 10.1016/j.nicl.2019.101782] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2018] [Revised: 02/07/2019] [Accepted: 03/14/2019] [Indexed: 01/12/2023]
Abstract
BACKGROUND In the United States, 13% of adults are estimated to have alcohol use disorder (AUD). Most studies examining the neurobiology of AUD treat individuals with this disorder as a homogeneous group; however, the theories of the neurocircuitry of AUD call for a quantitative and dimensional approach. Previous imaging studies find differences in brain structure, function, and resting-state connectivity in AUD, but few use a multimodal approach to understand the association between severity of alcohol use and the brain differences. METHODS Adults (ages 22-60) with problem drinking patterns (n = 59) completed a behavioral and neuroimaging protocol at the National Institutes of Health. Alcohol severity was quantified with the Alcohol Use Disorders Identification Test (AUDIT). In a 3 T MRI scanner, participants underwent a structural MRI as well as resting-state, monetary incentive delay, and face matching fMRI scans. Machine learning was applied and trained using the neural data from MRI scanning. The model was tested for generalizability in a validation sample (n = 24). RESULTS The resting state-connectivity features model best predicted AUD severity in the naïve sample, compared to task fMRI, structural MRI, combined MRI features, or demographic features. Network connectivity features between salience network, default mode network, executive control network, and sensory networks explained 33% of the variance associated with AUDIT in this model. CONCLUSIONS These findings indicate that the neural effects of AUD vary according to severity. Our results emphasize the utility of resting state fMRI as a neuroimaging biomarker for quantitative clinical evaluation of AUD.
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Affiliation(s)
- Samantha J Fede
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
| | - Erica N Grodin
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Sarah F Dean
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Nancy Diazgranados
- Office of Clinical Director, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States
| | - Reza Momenan
- Clinical NeuroImaging Research Core, National Institute of Alcohol Abuse and Alcoholism, National Institutes of Health, 10 Center Drive, Bethesda, MD 20814, MSC 1108, United States.
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Ottoy J, Niemantsverdriet E, Verhaeghe J, De Roeck E, Struyfs H, Somers C, Wyffels L, Ceyssens S, Van Mossevelde S, Van den Bossche T, Van Broeckhoven C, Ribbens A, Bjerke M, Stroobants S, Engelborghs S, Staelens S. Association of short-term cognitive decline and MCI-to-AD dementia conversion with CSF, MRI, amyloid- and 18F-FDG-PET imaging. NEUROIMAGE-CLINICAL 2019; 22:101771. [PMID: 30927601 PMCID: PMC6444289 DOI: 10.1016/j.nicl.2019.101771] [Citation(s) in RCA: 95] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 01/08/2019] [Accepted: 03/10/2019] [Indexed: 12/31/2022]
Abstract
Disease-modifying treatment trials are increasingly advanced to the prodromal or preclinical phase of Alzheimer's disease (AD), and inclusion criteria are based on biomarkers rather than clinical symptoms. Therefore, it is of great interest to determine which biomarkers should be combined to accurately predict conversion from mild cognitive impairment (MCI) to AD dementia. However, up to date, only few studies performed a complete A/T/N subject characterization using each of the CSF and imaging markers, or they only investigated long-term (≥ 2 years) prognosis. This study aimed to investigate the association between cerebrospinal fluid (CSF), magnetic resonance imaging (MRI), amyloid- and 18F-FDG positron emission tomography (PET) measures at baseline, in relation to cognitive changes and conversion to AD dementia over a short-term (12-month) period. We included 13 healthy controls, 49 MCI and 16 AD dementia patients with a clinical-based diagnosis and a complete A/T/N characterization at baseline. Global cortical amyloid-β (Aβ) burden was quantified using the 18F-AV45 standardized uptake value ratio (SUVR) with two different reference regions (cerebellar grey and subcortical white matter), whereas metabolism was assessed based on 18F-FDG SUVR. CSF measures included Aβ1–42, Aβ1–40, T-tau, P-tau181, and their ratios, and MRI markers included hippocampal volumes (HV), white matter hyperintensities, and cortical grey matter volumes. Cognitive functioning was measured by MMSE and RBANS index scores. All statistical analyses were corrected for age, sex, education, and APOE ε4 genotype. As a result, faster cognitive decline was most strongly associated with hypometabolism (posterior cingulate) and smaller hippocampal volume (e.g., Δstory recall: β = +0.43 [p < 0.001] and + 0.37 [p = 0.005], resp.) at baseline. In addition, faster cognitive decline was significantly associated with higher baseline Aβ burden only if SUVR was referenced to the subcortical white matter (e.g., Δstory recall: β = −0.28 [p = 0.020]). Patients with MCI converted to AD dementia at an annual rate of 31%, which could be best predicted by combining neuropsychological testing (visuospatial construction skills) with either MRI-based HV or 18F-FDG-PET. Combining all three markers resulted in 96% specificity and 92% sensitivity. Neither amyloid-PET nor CSF biomarkers could discriminate short-term converters from non-converters. FDG-PET and MRI HV are the strongest predictors of cognitive decline and conversion to AD. Combination of visuospatial construction testing with FDG-PET or MRI HV present high predicting power of conversion. CSF and amyloid-PET seem less suitable markers of disease progression. Increased AV45-PET predicts short-term cognitive decline if SUVR is referenced to WM instead of CB.
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Affiliation(s)
- Julie Ottoy
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Jeroen Verhaeghe
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Charisse Somers
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Leonie Wyffels
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sarah Ceyssens
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sara Van Mossevelde
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Tobi Van den Bossche
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium; Laboratory of Neurogenetics, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | | | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Sigrid Stroobants
- Department of Nuclear Medicine, Antwerp University Hospital, Edegem, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Laboratory of Neurochemistry and Behavior, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium
| | - Steven Staelens
- Molecular Imaging Center Antwerp, University of Antwerp, Antwerp, Belgium.
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Cheng B, Liu M, Zhang D, Shen D. Robust multi-label transfer feature learning for early diagnosis of Alzheimer's disease. Brain Imaging Behav 2019; 13:138-153. [PMID: 29589326 PMCID: PMC8162712 DOI: 10.1007/s11682-018-9846-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Transfer learning has been successfully used in the early diagnosis of Alzheimer's disease (AD). In these methods, data from one single or multiple related source domain(s) are employed to aid the learning task in the target domain. However, most of the existing methods utilize data from all source domains, ignoring the fact that unrelated source domains may degrade the learning performance. Also, previous studies assume that class labels for all subjects are reliable, without considering the ambiguity of class labels caused by slight differences between early AD patients and normal control subjects. To address these issues, we propose to transform the original binary class label of a particular subject into a multi-bit label coding vector with the aid of multiple source domains. We further develop a robust multi-label transfer feature learning (rMLTFL) model to simultaneously capture a common set of features from different domains (including the target domain and all source domains) and to identify the unrelated source domains. We evaluate our method on 406 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database with baseline magnetic resonance imaging (MRI) and cerebrospinal fluid (CSF) data. The experimental results show that the proposed rMLTFL method can effectively improve the performance of AD diagnosis, compared with several state-of-the-art methods.
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Affiliation(s)
- Bo Cheng
- Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing, 404100, China
- Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology, Chongqing Three Gorges University, Chongqing, 404100, China
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Daoqiang Zhang
- Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.
- Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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Femminella GD, Thayanandan T, Calsolaro V, Komici K, Rengo G, Corbi G, Ferrara N. Imaging and Molecular Mechanisms of Alzheimer's Disease: A Review. Int J Mol Sci 2018; 19:E3702. [PMID: 30469491 PMCID: PMC6321449 DOI: 10.3390/ijms19123702] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 11/13/2018] [Accepted: 11/14/2018] [Indexed: 02/07/2023] Open
Abstract
Alzheimer's disease is the most common form of dementia and is a significant burden for affected patients, carers, and health systems. Great advances have been made in understanding its pathophysiology, to a point that we are moving from a purely clinical diagnosis to a biological one based on the use of biomarkers. Among those, imaging biomarkers are invaluable in Alzheimer's, as they provide an in vivo window to the pathological processes occurring in Alzheimer's brain. While some imaging techniques are still under evaluation in the research setting, some have reached widespread clinical use. In this review, we provide an overview of the most commonly used imaging biomarkers in Alzheimer's disease, from molecular PET imaging to structural MRI, emphasising the concept that multimodal imaging would likely prove to be the optimal tool in the future of Alzheimer's research and clinical practice.
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Affiliation(s)
| | - Tony Thayanandan
- Imperial Memory Unit, Charing Cross Hospital, Imperial College London, London W6 8RF, UK.
| | - Valeria Calsolaro
- Neurology Imaging Unit, Imperial College London, London W12 0NN, UK.
| | - Klara Komici
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
| | - Graziamaria Corbi
- Department of Medicine and Health Sciences, University of Molise, 86100 Campobasso, Italy.
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, 80131 Naples, Italy.
- Istituti Clinici Scientifici Maugeri SPA-Società Benefit, IRCCS, 82037 Telese Terme, Italy.
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50
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Huang CC, Huang WM, Chen CH, Jhou ZY, The Alzheimer's Disease Neuroimaging Initiative, Lin CP. The Combination of Functional and Structural MRI Is a Potential Screening Tool in Alzheimer's Disease. Front Aging Neurosci 2018; 10:251. [PMID: 30297997 PMCID: PMC6160579 DOI: 10.3389/fnagi.2018.00251] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Accepted: 07/31/2018] [Indexed: 11/13/2022] Open
Abstract
Introduction: This study aimed to survey the discrimination power of parameters from cerebrospinal fluid (CSF) biomarkers, fluorodeoxyglucose uptake on PET (FDG-PET), structural magnetic resonance imaging (MRI), and functional MRI in high- and low-risk subjects or in converters and stable subjects of normal and mild cognitive impairment (MCI) statuses. Methods: We used baseline resting-state functional MRI (rfMRI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to analyze functional networks and recorded subjects' characteristics and results of the CSF study, FDG-PET, and structural MRI from the ADNI website. All parameters were evaluated based on the between-group difference among normal (NC), MCI, and Alzheimer's disease (AD) groups. The parameters other than CSF results were included to study the difference between high- and low-AD-risk subjects in NC or MCI groups, based on CSF results. On the basis of two-year follow-up conditions, all parameters were compared between stable subjects and converters in NC and MCI. Results: CSF biomarkers, FDG-PET, structural MRI, and functional MRI are all able to differentiate AD from MCI or NC but not between MCI and NC. As compared with low-AD-risk subjects, high-risk subjects present decreased FDG-PET in both MCI and NC groups but structural MRI change only in MCI status and rfMRI alteration only in NC status. As compared with stable subjects, converters have decreased FDG-PET, functional network changes, and structural changes in both MCI and NC groups. Conclusion: The combination of functional and structural MRI is a safer screening tool but with similar power as FDG-PET to reflect CSF change in the AD pathological process and to identify high-risk subjects and converters in NC and MCI.
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Affiliation(s)
- Chun-Chao Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, Mackay Medical College, Taipei, Taiwan.,Mackay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan
| | - Wei-Ming Huang
- Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, Mackay Medical College, Taipei, Taiwan.,Mackay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan
| | - Chia-Hung Chen
- Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, Mackay Medical College, Taipei, Taiwan.,Mackay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan
| | - Zong-Yi Jhou
- Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, Mackay Medical College, Taipei, Taiwan.,Mackay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan
| | - The Alzheimer's Disease Neuroimaging Initiative
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Radiology, Mackay Memorial Hospital, Taipei, Taiwan.,Department of Medicine, Mackay Medical College, Taipei, Taiwan.,Mackay Junior College of Medicine, Nursing, and Management, Taipei, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
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